In [1]:
#!pip install catboost
In [2]:
import pandas as pd
from pyspark.ml.regression import DecisionTreeRegressor
from pyspark.ml.classification import DecisionTreeClassifier, RandomForestClassifier, GBTClassifier
from pyspark.ml.regression import RandomForestRegressor, RandomForestRegressionModel
from pyspark.ml.regression import GBTRegressor, GBTRegressionModel
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics.regression import mean_squared_error
In [3]:
boston = load_boston()
X = boston['data']
y = boston['target']
feature_names = boston['feature_names']
feature_descr = boston['DESCR']
In [4]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)
In [5]:
feature_names.tolist()+['MV']
Out[5]:
['CRIM',
'ZN',
'INDUS',
'CHAS',
'NOX',
'RM',
'AGE',
'DIS',
'RAD',
'TAX',
'PTRATIO',
'B',
'LSTAT',
'MV']
In [6]:
train_df = sqlContext.createDataFrame(pd.DataFrame(pd.np.hstack([X_train, y_train.reshape(-1,1)]), columns=feature_names.tolist()+['MV']))
test_df = sqlContext.createDataFrame(pd.DataFrame(pd.np.hstack([X_test, y_test.reshape(-1,1)]), columns=feature_names.tolist()+['MV']))
In [7]:
test_df.show()
+-------+----+-----+----+-----+-----+-----+------+----+-----+-------+------+-----+----+
| CRIM| ZN|INDUS|CHAS| NOX| RM| AGE| DIS| RAD| TAX|PTRATIO| B|LSTAT| MV|
+-------+----+-----+----+-----+-----+-----+------+----+-----+-------+------+-----+----+
|0.09178| 0.0| 4.05| 0.0| 0.51|6.416| 84.1|2.6463| 5.0|296.0| 16.6| 395.5| 9.04|23.6|
|0.05644|40.0| 6.41| 1.0|0.447|6.758| 32.9|4.0776| 4.0|254.0| 17.6| 396.9| 3.53|32.4|
|0.10574| 0.0|27.74| 0.0|0.609|5.983| 98.8|1.8681| 4.0|711.0| 20.1|390.11|18.07|13.6|
|0.09164| 0.0|10.81| 0.0|0.413|6.065| 7.8|5.2873| 4.0|305.0| 19.2|390.91| 5.52|22.8|
|5.09017| 0.0| 18.1| 0.0|0.713|6.297| 91.8|2.3682|24.0|666.0| 20.2|385.09|17.27|16.1|
|0.10153| 0.0|12.83| 0.0|0.437|6.279| 74.5|4.0522| 5.0|398.0| 18.7|373.66|11.97|20.0|
|0.31827| 0.0| 9.9| 0.0|0.544|5.914| 83.2|3.9986| 4.0|304.0| 18.4| 390.7|18.33|17.8|
| 0.2909| 0.0|21.89| 0.0|0.624|6.174| 93.6|1.6119| 4.0|437.0| 21.2|388.08|24.16|14.0|
|4.03841| 0.0| 18.1| 0.0|0.532|6.229| 90.7|3.0993|24.0|666.0| 20.2|395.33|12.87|19.6|
|0.22438| 0.0| 9.69| 0.0|0.585|6.027| 79.7|2.4982| 6.0|391.0| 19.2| 396.9|14.33|16.8|
|0.11069| 0.0|13.89| 1.0| 0.55|5.951| 93.8|2.8893| 5.0|276.0| 16.4| 396.9|17.92|21.5|
|0.17004|12.5| 7.87| 0.0|0.524|6.004| 85.9|6.5921| 5.0|311.0| 15.2|386.71| 17.1|18.9|
|45.7461| 0.0| 18.1| 0.0|0.693|4.519|100.0|1.6582|24.0|666.0| 20.2| 88.27|36.98| 7.0|
|0.05646| 0.0|12.83| 0.0|0.437|6.232| 53.7|5.0141| 5.0|398.0| 18.7| 386.4|12.34|21.2|
|0.28392| 0.0| 7.38| 0.0|0.493|5.708| 74.3|4.7211| 5.0|287.0| 19.6|391.13|11.74|18.5|
|4.64689| 0.0| 18.1| 0.0|0.614| 6.98| 67.6|2.5329|24.0|666.0| 20.2|374.68|11.66|29.8|
|0.09849| 0.0|25.65| 0.0|0.581|5.879| 95.8|2.0063| 2.0|188.0| 19.1|379.38|17.58|18.8|
|14.3337| 0.0| 18.1| 0.0| 0.7| 4.88|100.0|1.5895|24.0|666.0| 20.2|372.92|30.62|10.2|
|0.01381|80.0| 0.46| 0.0|0.422|7.875| 32.0|5.6484| 4.0|255.0| 14.4|394.23| 2.97|50.0|
|9.32909| 0.0| 18.1| 0.0|0.713|6.185| 98.7|2.2616|24.0|666.0| 20.2| 396.9|18.13|14.1|
+-------+----+-----+----+-----+-----+-----+------+----+-----+-------+------+-----+----+
only showing top 20 rows
In [8]:
test_df.count(), train_df.count()
Out[8]:
(152, 354)
In [9]:
from pyspark.ml.feature import VectorAssembler
vectorAssembler = VectorAssembler(inputCols = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT'], outputCol = 'features')
vtrain_df = vectorAssembler.transform(train_df)
vtrain_df = vtrain_df.select(['features', 'MV'])
vtrain_df.show(3)
vtest_df = vectorAssembler.transform(test_df)
vtest_df = vtest_df.select(['features', 'MV'])
vtest_df.show(3)
+--------------------+----+
| features| MV|
+--------------------+----+
|[0.02985,0.0,2.18...|28.7|
|[0.13158,0.0,10.0...|21.2|
|[0.17142,0.0,6.91...|19.3|
+--------------------+----+
only showing top 3 rows
+--------------------+----+
| features| MV|
+--------------------+----+
|[0.09178,0.0,4.05...|23.6|
|[0.05644,40.0,6.4...|32.4|
|[0.10574,0.0,27.7...|13.6|
+--------------------+----+
only showing top 3 rows
In [10]:
from pyspark.ml.feature import VectorIndexer
featureIndexer =\
VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(vtrain_df.union(vtest_df))
In [11]:
dtr = DecisionTreeRegressor(labelCol="MV", maxDepth=5, maxBins=255)
#model = dtr.fit(vtrain_df)
In [12]:
from pyspark.ml import Pipeline
pipeline = Pipeline(stages=[featureIndexer, dtr])
In [13]:
model = pipeline.fit(vtrain_df)
In [14]:
print(model.stages[1].numNodes)
print(model.stages[1].featureImportances)
53
(13,[0,1,2,4,5,6,7,10,11,12],[0.04625407144587292,0.0060210496461917265,0.001309271045741982,0.005320665051531463,0.6035694575660526,0.005645651290926141,0.10727180508818107,0.024130674232067544,0.0072224726975601275,0.19325488193587448])
In [15]:
model.stages[1].toDebugString
Out[15]:
'DecisionTreeRegressionModel (uid=DecisionTreeRegressor_4cbd9830b690a8b5d6b9) of depth 5 with 53 nodes\n If (feature 5 <= 6.941)\n If (feature 12 <= 14.399999999999999)\n If (feature 7 <= 1.35735)\n If (feature 0 <= 8.37969)\n Predict: 50.0\n Else (feature 0 > 8.37969)\n Predict: 27.900000000000006\n Else (feature 7 > 1.35735)\n If (feature 5 <= 6.543)\n If (feature 12 <= 7.76)\n Predict: 23.776\n Else (feature 12 > 7.76)\n Predict: 21.144444444444435\n Else (feature 5 > 6.543)\n If (feature 12 <= 5.6899999999999995)\n Predict: 30.00714285714286\n Else (feature 12 > 5.6899999999999995)\n Predict: 25.985185185185177\n Else (feature 12 > 14.399999999999999)\n If (feature 7 <= 2.0754)\n If (feature 0 <= 6.340595)\n If (feature 12 <= 15.47)\n Predict: 18.733333333333334\n Else (feature 12 > 15.47)\n Predict: 14.325\n Else (feature 0 > 6.340595)\n If (feature 4 <= 0.675)\n Predict: 12.972727272727274\n Else (feature 4 > 0.675)\n Predict: 9.825000000000001\n Else (feature 7 > 2.0754)\n If (feature 11 <= 311.205)\n If (feature 12 <= 17.375)\n Predict: 15.866666666666665\n Else (feature 12 > 17.375)\n Predict: 13.5\n Else (feature 11 > 311.205)\n If (feature 6 <= 84.55)\n Predict: 20.700000000000003\n Else (feature 6 > 84.55)\n Predict: 17.818181818181817\n Else (feature 5 > 6.941)\n If (feature 5 <= 7.418)\n If (feature 0 <= 6.59684)\n If (feature 7 <= 1.88595)\n If (feature 2 <= 18.84)\n Predict: 50.0\n Else (feature 2 > 18.84)\n Predict: 41.3\n Else (feature 7 > 1.88595)\n If (feature 10 <= 19.4)\n Predict: 32.987500000000004\n Else (feature 10 > 19.4)\n Predict: 24.450000000000045\n Else (feature 0 > 6.59684)\n If (feature 4 <= 0.675)\n Predict: 10.4\n Else (feature 4 > 0.675)\n Predict: 17.800000000000004\n Else (feature 5 > 7.418)\n If (feature 10 <= 18.1)\n If (feature 7 <= 2.4218)\n If (feature 0 <= 0.53556)\n Predict: 45.95\n Else (feature 0 > 0.53556)\n Predict: 50.0\n Else (feature 7 > 2.4218)\n If (feature 6 <= 70.5)\n Predict: 46.3875\n Else (feature 6 > 70.5)\n Predict: 40.69999999999999\n Else (feature 10 > 18.1)\n If (feature 1 <= 6.25)\n Predict: 21.9\n Else (feature 1 > 6.25)\n If (feature 4 <= 0.432)\n Predict: 42.8\n Else (feature 4 > 0.432)\n Predict: 34.300000000000004\n'
In [16]:
model.stages[1].save('BostonDT')
model_df = spark.read.parquet("BostonDT/data")
print(model_df.printSchema())
root
|-- id: integer (nullable = true)
|-- prediction: double (nullable = true)
|-- impurity: double (nullable = true)
|-- impurityStats: array (nullable = true)
| |-- element: double (containsNull = true)
|-- gain: double (nullable = true)
|-- leftChild: integer (nullable = true)
|-- rightChild: integer (nullable = true)
|-- split: struct (nullable = true)
| |-- featureIndex: integer (nullable = true)
| |-- leftCategoriesOrThreshold: array (nullable = true)
| | |-- element: double (containsNull = true)
| |-- numCategories: integer (nullable = true)
None
In [17]:
model_df.toJSON().collect()
Out[17]:
['{"id":0,"prediction":23.015819209039552,"impurity":87.89652941364231,"impurityStats":[354.0,8147.600000000001,218639.06000000006],"gain":40.484981224398176,"leftChild":1,"rightChild":28,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[6.941],"numCategories":-1}}',
'{"id":1,"prediction":20.08390410958904,"impurity":41.294501196284266,"impurityStats":[292.0,5864.499999999999,129840.04999999992],"gain":17.898934791141883,"leftChild":2,"rightChild":13,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[14.399999999999999],"numCategories":-1}}',
'{"id":2,"prediction":23.469662921348323,"impurity":26.11368640323184,"impurityStats":[178.0,4177.600000000001,102695.10000000003],"gain":10.14308473075324,"leftChild":3,"rightChild":6,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[1.35735],"numCategories":-1}}',
'{"id":3,"prediction":44.475,"impurity":91.57687499999975,"impurityStats":[4.0,177.9,8278.41],"gain":91.57687499999986,"leftChild":4,"rightChild":5,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[8.37969],"numCategories":-1}}',
'{"id":4,"prediction":50.0,"impurity":0.0,"impurityStats":[3.0,150.0,7500.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":5,"prediction":27.900000000000006,"impurity":-1.0,"impurityStats":[1.0,27.900000000000006,778.4099999999999],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":6,"prediction":22.986781609195397,"impurity":14.23252642357007,"impurityStats":[174.0,3999.6999999999994,94416.69000000002],"gain":5.891743642395225,"leftChild":7,"rightChild":10,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[6.543],"numCategories":-1}}',
'{"id":7,"prediction":21.6390977443609,"impurity":7.17426084007019,"impurityStats":[133.0,2877.9999999999995,63231.49999999999],"gain":1.0570257523506053,"leftChild":8,"rightChild":9,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[7.76],"numCategories":-1}}',
'{"id":8,"prediction":23.776,"impurity":2.2794240000000716,"impurityStats":[25.0,594.4,14189.44],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":9,"prediction":21.144444444444435,"impurity":7.005617283950953,"impurityStats":[108.0,2283.599999999999,49042.05999999999],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":10,"prediction":27.35853658536586,"impurity":12.1248661510997,"impurityStats":[41.0,1121.7000000000003,31185.18999999998],"gain":3.6374671318712117,"leftChild":11,"rightChild":12,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[5.6899999999999995],"numCategories":-1}}',
'{"id":11,"prediction":30.00714285714286,"impurity":8.642091836734705,"impurityStats":[14.0,420.1,12726.990000000002],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":12,"prediction":25.985185185185177,"impurity":8.40718792866971,"impurityStats":[27.0,701.5999999999998,18458.199999999997],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":13,"prediction":14.797368421052628,"impurity":19.151484302862357,"impurityStats":[114.0,1686.8999999999996,27144.949999999983],"gain":7.639321467112913,"leftChild":14,"rightChild":21,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[2.0754],"numCategories":-1}}',
'{"id":14,"prediction":12.266129032258062,"impurity":12.16159469302813,"impurityStats":[62.0,760.4999999999999,10082.41],"gain":4.091214213292885,"leftChild":15,"rightChild":18,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[6.340595],"numCategories":-1}}',
'{"id":15,"prediction":14.900000000000002,"impurity":6.959999999999917,"impurityStats":[23.0,342.70000000000005,5266.3099999999995],"gain":2.2041666666666613,"leftChild":16,"rightChild":17,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[15.47],"numCategories":-1}}',
'{"id":16,"prediction":18.733333333333334,"impurity":3.7955555555554383,"impurityStats":[3.0,56.2,1064.1999999999998],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":17,"prediction":14.325,"impurity":4.899874999999929,"impurityStats":[20.0,286.5,4202.109999999999],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":18,"prediction":10.712820512820512,"impurity":8.725220249835566,"impurityStats":[39.0,417.79999999999995,4816.099999999997],"gain":2.0063915785069053,"leftChild":19,"rightChild":20,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[0.675],"numCategories":-1}}',
'{"id":19,"prediction":12.972727272727274,"impurity":3.0819834710743264,"impurityStats":[11.0,142.70000000000002,1885.11],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":20,"prediction":9.825000000000001,"impurity":8.147589285714291,"impurityStats":[28.0,275.1,2930.9900000000007],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":21,"prediction":17.815384615384616,"impurity":10.737840236686399,"impurityStats":[52.0,926.4,17062.54],"gain":4.014777772834655,"leftChild":22,"rightChild":25,"split":{"featureIndex":11,"leftCategoriesOrThreshold":[311.205],"numCategories":-1}}',
'{"id":22,"prediction":14.514285714285718,"impurity":2.874081632652925,"impurityStats":[14.0,203.20000000000005,2989.5399999999995],"gain":1.371700680271975,"leftChild":23,"rightChild":24,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[17.375],"numCategories":-1}}',
'{"id":23,"prediction":15.866666666666665,"impurity":3.31888888888894,"impurityStats":[6.0,95.19999999999999,1530.4199999999998],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":24,"prediction":13.5,"impurity":0.13999999999995794,"impurityStats":[8.0,108.0,1459.1199999999997],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":25,"prediction":19.031578947368427,"impurity":8.141108033240782,"impurityStats":[38.0,723.2000000000003,14073.000000000002],"gain":2.024457315537491,"leftChild":26,"rightChild":27,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[84.55],"numCategories":-1}}',
'{"id":26,"prediction":20.700000000000003,"impurity":3.1462499999998954,"impurityStats":[16.0,331.20000000000005,6906.18],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":27,"prediction":17.818181818181817,"impurity":8.276942148760305,"impurityStats":[22.0,392.0,7166.82],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":28,"prediction":36.8241935483871,"impurity":76.22086628511968,"impurityStats":[62.0,2283.1000000000004,88799.01000000002],"gain":33.70271511081346,"leftChild":29,"rightChild":40,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[7.418],"numCategories":-1}}',
'{"id":29,"prediction":32.21052631578947,"impurity":40.45620498614957,"impurityStats":[38.0,1224.0,40963.02],"gain":18.221731301939492,"leftChild":30,"rightChild":37,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[6.59684],"numCategories":-1}}',
'{"id":30,"prediction":33.21666666666667,"impurity":22.70916666666623,"impurityStats":[36.0,1195.8000000000002,40538.02],"gain":9.093398692809918,"leftChild":31,"rightChild":34,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[1.88595],"numCategories":-1}}',
'{"id":31,"prediction":45.65,"impurity":18.922500000000127,"impurityStats":[2.0,91.3,4205.69],"gain":18.92250000000024,"leftChild":32,"rightChild":33,"split":{"featureIndex":2,"leftCategoriesOrThreshold":[18.84],"numCategories":-1}}',
'{"id":32,"prediction":50.0,"impurity":0.0,"impurityStats":[1.0,50.0,2500.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":33,"prediction":41.3,"impurity":-2.2737367544323206E-13,"impurityStats":[1.0,41.3,1705.6899999999996],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":34,"prediction":32.48529411764706,"impurity":13.303607266436089,"impurityStats":[34.0,1104.5,36332.33],"gain":4.035371972318611,"leftChild":35,"rightChild":36,"split":{"featureIndex":10,"leftCategoriesOrThreshold":[19.4],"numCategories":-1}}',
'{"id":35,"prediction":32.987500000000004,"impurity":9.828593749999982,"impurityStats":[32.0,1055.6000000000001,35136.12000000001],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":36,"prediction":24.450000000000045,"impurity":0.3024999999973943,"impurityStats":[2.0,48.90000000000009,1196.2099999999991],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":37,"prediction":14.100000000000023,"impurity":13.689999999999372,"impurityStats":[2.0,28.200000000000045,425.0],"gain":13.68999999999943,"leftChild":38,"rightChild":39,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[0.675],"numCategories":-1}}',
'{"id":38,"prediction":10.4,"impurity":0.0,"impurityStats":[1.0,10.4,108.16000000000001],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":39,"prediction":17.800000000000004,"impurity":-1.0,"impurityStats":[1.0,17.800000000000004,316.84000000000003],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":40,"prediction":44.129166666666684,"impurity":45.78289930555426,"impurityStats":[24.0,1059.1000000000004,47835.99],"gain":23.346003472221774,"leftChild":41,"rightChild":48,"split":{"featureIndex":10,"leftCategoriesOrThreshold":[18.1],"numCategories":-1}}',
'{"id":41,"prediction":46.290000000000006,"impurity":15.732899999999427,"impurityStats":[20.0,925.8000000000001,43169.939999999995],"gain":4.851004166666463,"leftChild":42,"rightChild":45,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[2.4218],"numCategories":-1}}',
'{"id":42,"prediction":48.987500000000004,"impurity":5.106093749999673,"impurityStats":[8.0,391.90000000000003,19239.05],"gain":3.0754687499998,"leftChild":43,"rightChild":44,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[0.53556],"numCategories":-1}}',
'{"id":43,"prediction":45.95,"impurity":8.12249999999949,"impurityStats":[2.0,91.9,4239.049999999999],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":44,"prediction":50.0,"impurity":0.0,"impurityStats":[6.0,300.0,15000.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":45,"prediction":44.491666666666674,"impurity":14.73243055555516,"impurityStats":[12.0,533.9000000000001,23930.890000000003],"gain":7.188368055555126,"leftChild":46,"rightChild":47,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[70.5],"numCategories":-1}}',
'{"id":46,"prediction":46.3875,"impurity":7.388593749999927,"impurityStats":[8.0,371.1,17273.510000000002],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":47,"prediction":40.69999999999999,"impurity":7.855000000000246,"impurityStats":[4.0,162.79999999999995,6657.379999999997],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":48,"prediction":33.32500000000002,"impurity":55.95687499999781,"impurityStats":[4.0,133.30000000000007,4666.049999999996],"gain":43.51020833333132,"leftChild":49,"rightChild":50,"split":{"featureIndex":1,"leftCategoriesOrThreshold":[6.25],"numCategories":-1}}',
'{"id":49,"prediction":21.9,"impurity":0.0,"impurityStats":[1.0,21.9,479.60999999999996],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":50,"prediction":37.13333333333333,"impurity":16.59555555555532,"impurityStats":[3.0,111.4,4186.44],"gain":16.055555555555202,"leftChild":51,"rightChild":52,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[0.432],"numCategories":-1}}',
'{"id":51,"prediction":42.8,"impurity":0.0,"impurityStats":[1.0,42.8,1831.8399999999997],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":52,"prediction":34.300000000000004,"impurity":0.8100000000001728,"impurityStats":[2.0,68.60000000000001,2354.600000000001],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}']
In [18]:
predictions = model.transform(vtest_df)
In [19]:
# Evaluate model on test instances and compute test error
predictions.select("prediction","MV","features").show(5)
from pyspark.ml.evaluation import RegressionEvaluator
evaluator = RegressionEvaluator(predictionCol="prediction", \
labelCol="MV",metricName="r2")
print("R Squared (R2) on test data = %g" % evaluator.evaluate(predictions))
+------------------+----+--------------------+
| prediction| MV| features|
+------------------+----+--------------------+
|21.144444444444435|23.6|[0.09178,0.0,4.05...|
| 30.00714285714286|32.4|[0.05644,40.0,6.4...|
| 14.325|13.6|[0.10574,0.0,27.7...|
| 23.776|22.8|[0.09164,0.0,10.8...|
|17.818181818181817|16.1|[5.09017,0.0,18.1...|
+------------------+----+--------------------+
only showing top 5 rows
R Squared (R2) on test data = 0.80403
In [20]:
gbt = GBTRegressor(labelCol="MV", maxDepth=5, maxBins=255)
pipeline = Pipeline(stages=[featureIndexer, gbt])
model = pipeline.fit(vtrain_df)
In [21]:
print(model.stages[1].getNumTrees)
print(model.stages[1].featureImportances)
20
(13,[0,1,2,3,4,5,6,7,8,9,10,11,12],[0.1756225280294118,0.029639026595062644,0.06619357981561497,0.002846418260275094,0.05666757586755877,0.16189482738545105,0.15098604448624736,0.09437270013759662,0.011082802768327371,0.08281989512434737,0.0539263365865294,0.05041290409319589,0.06353536085038167])
In [22]:
model.stages[1].toDebugString
Out[22]:
'GBTRegressionModel (uid=GBTRegressor_4459aff116e36a86b51b) with 20 trees\n Tree 0 (weight 1.0):\n If (feature 5 <= 6.941)\n If (feature 12 <= 14.399999999999999)\n If (feature 7 <= 1.35735)\n If (feature 0 <= 8.37969)\n Predict: 50.0\n Else (feature 0 > 8.37969)\n Predict: 27.900000000000006\n Else (feature 7 > 1.35735)\n If (feature 5 <= 6.543)\n If (feature 12 <= 7.76)\n Predict: 23.776\n Else (feature 12 > 7.76)\n Predict: 21.144444444444435\n Else (feature 5 > 6.543)\n If (feature 12 <= 5.6899999999999995)\n Predict: 30.00714285714286\n Else (feature 12 > 5.6899999999999995)\n Predict: 25.985185185185177\n Else (feature 12 > 14.399999999999999)\n If (feature 7 <= 2.0754)\n If (feature 0 <= 6.340595)\n If (feature 12 <= 15.47)\n Predict: 18.733333333333334\n Else (feature 12 > 15.47)\n Predict: 14.325\n Else (feature 0 > 6.340595)\n If (feature 4 <= 0.675)\n Predict: 12.972727272727274\n Else (feature 4 > 0.675)\n Predict: 9.825000000000001\n Else (feature 7 > 2.0754)\n If (feature 11 <= 311.205)\n If (feature 12 <= 17.375)\n Predict: 15.866666666666665\n Else (feature 12 > 17.375)\n Predict: 13.5\n Else (feature 11 > 311.205)\n If (feature 6 <= 84.55)\n Predict: 20.700000000000003\n Else (feature 6 > 84.55)\n Predict: 17.818181818181817\n Else (feature 5 > 6.941)\n If (feature 5 <= 7.418)\n If (feature 0 <= 6.59684)\n If (feature 7 <= 1.88595)\n If (feature 2 <= 18.84)\n Predict: 50.0\n Else (feature 2 > 18.84)\n Predict: 41.3\n Else (feature 7 > 1.88595)\n If (feature 10 <= 19.4)\n Predict: 32.987500000000004\n Else (feature 10 > 19.4)\n Predict: 24.450000000000045\n Else (feature 0 > 6.59684)\n If (feature 4 <= 0.675)\n Predict: 10.4\n Else (feature 4 > 0.675)\n Predict: 17.800000000000004\n Else (feature 5 > 7.418)\n If (feature 10 <= 18.1)\n If (feature 7 <= 2.4218)\n If (feature 0 <= 0.53556)\n Predict: 45.95\n Else (feature 0 > 0.53556)\n Predict: 50.0\n Else (feature 7 > 2.4218)\n If (feature 6 <= 70.5)\n Predict: 46.3875\n Else (feature 6 > 70.5)\n Predict: 40.69999999999999\n Else (feature 10 > 18.1)\n If (feature 1 <= 6.25)\n Predict: 21.9\n Else (feature 1 > 6.25)\n If (feature 4 <= 0.432)\n Predict: 42.8\n Else (feature 4 > 0.432)\n Predict: 34.300000000000004\n Tree 1 (weight 0.1):\n If (feature 9 <= 222.5)\n If (feature 11 <= 384.5)\n If (feature 0 <= 0.073735)\n If (feature 0 <= 0.017435)\n Predict: 0.18571428571428328\n Else (feature 0 > 0.017435)\n If (feature 0 <= 0.061399999999999996)\n Predict: -0.7750000000000057\n Else (feature 0 > 0.061399999999999996)\n Predict: -1.6888888888888687\n Else (feature 0 > 0.073735)\n If (feature 0 <= 0.093385)\n Predict: 5.363636363636367\n Else (feature 0 > 0.093385)\n Predict: 5.950000000000003\n Else (feature 11 > 384.5)\n If (feature 10 <= 17.85)\n If (feature 11 <= 387.245)\n Predict: 16.911111111111133\n Else (feature 11 > 387.245)\n If (feature 6 <= 90.1)\n Predict: 8.714365079365079\n Else (feature 6 > 90.1)\n Predict: 4.98571428571428\n Else (feature 10 > 17.85)\n If (feature 6 <= 58.75)\n If (feature 12 <= 2.95)\n Predict: 0.8249999999999886\n Else (feature 12 > 2.95)\n Predict: 6.831907407407408\n Else (feature 6 > 58.75)\n If (feature 0 <= 0.07689)\n Predict: -0.021296296296296664\n Else (feature 0 > 0.07689)\n Predict: 5.333333333333329\n Else (feature 9 > 222.5)\n If (feature 0 <= 0.056975)\n If (feature 6 <= 79.05000000000001)\n If (feature 2 <= 12.379999999999999)\n If (feature 12 <= 8.434999999999999)\n Predict: -1.301743947410613\n Else (feature 12 > 8.434999999999999)\n Predict: -4.271759259259243\n Else (feature 2 > 12.379999999999999)\n If (feature 0 <= 0.04575)\n Predict: 3.911111111111129\n Else (feature 0 > 0.04575)\n Predict: 2.229629629629649\n Else (feature 6 > 79.05000000000001)\n If (feature 2 <= 3.425)\n Predict: -18.775000000000006\n Else (feature 2 > 3.425)\n Predict: -18.488888888888866\n Else (feature 0 > 0.056975)\n If (feature 10 <= 17.700000000000003)\n If (feature 5 <= 5.433)\n If (feature 0 <= 1.1669999999999998)\n Predict: -11.688888888888869\n Else (feature 0 > 1.1669999999999998)\n Predict: -2.377777777777772\n Else (feature 5 > 5.433)\n If (feature 11 <= 393.425)\n Predict: 1.1999155683822402\n Else (feature 11 > 393.425)\n Predict: 4.67884517443342\n Else (feature 10 > 17.700000000000003)\n If (feature 10 <= 18.45)\n If (feature 12 <= 4.845)\n Predict: -7.6008928571428545\n Else (feature 12 > 4.845)\n Predict: -2.0188892773892646\n Else (feature 10 > 18.45)\n If (feature 10 <= 19.4)\n Predict: 2.320414351851862\n Else (feature 10 > 19.4)\n Predict: -0.7152063279857366\n Tree 2 (weight 0.1):\n If (feature 9 <= 222.5)\n If (feature 11 <= 384.5)\n If (feature 0 <= 0.073735)\n If (feature 0 <= 0.017435)\n Predict: 0.14857142857142946\n Else (feature 0 > 0.017435)\n If (feature 0 <= 0.061399999999999996)\n Predict: -0.6200000000000045\n Else (feature 0 > 0.061399999999999996)\n Predict: -1.351111111111095\n Else (feature 0 > 0.073735)\n If (feature 0 <= 0.093385)\n Predict: 4.290909090909096\n Else (feature 0 > 0.093385)\n Predict: 4.760000000000002\n Else (feature 11 > 384.5)\n If (feature 10 <= 17.85)\n If (feature 11 <= 387.245)\n Predict: 13.528888888888908\n Else (feature 11 > 387.245)\n If (feature 6 <= 90.1)\n Predict: 6.971492063492063\n Else (feature 6 > 90.1)\n Predict: 3.9885714285714258\n Else (feature 10 > 17.85)\n If (feature 6 <= 58.75)\n If (feature 5 <= 6.432)\n Predict: 8.481618518518516\n Else (feature 5 > 6.432)\n Predict: 3.5101212962962958\n Else (feature 6 > 58.75)\n If (feature 1 <= 6.25)\n Predict: 2.9910185185185263\n Else (feature 1 > 6.25)\n Predict: -0.883240740740753\n Else (feature 9 > 222.5)\n If (feature 0 <= 0.066295)\n If (feature 6 <= 79.05000000000001)\n If (feature 2 <= 6.145)\n If (feature 12 <= 8.434999999999999)\n Predict: -0.9434006651678608\n Else (feature 12 > 8.434999999999999)\n Predict: -4.2001547343163885\n Else (feature 2 > 6.145)\n If (feature 3 <= 0.5)\n Predict: 1.5108796296296454\n Else (feature 3 > 0.5)\n Predict: 4.389417069858254\n Else (feature 6 > 79.05000000000001)\n If (feature 0 <= 0.056975)\n If (feature 0 <= 0.047799999999999995)\n Predict: -14.791111111111096\n Else (feature 0 > 0.047799999999999995)\n Predict: -15.020000000000003\n Else (feature 0 > 0.056975)\n Predict: -0.9569587344029458\n Else (feature 0 > 0.066295)\n If (feature 10 <= 16.5)\n If (feature 0 <= 0.144655)\n If (feature 0 <= 0.14167)\n Predict: 3.6443659892105043\n Else (feature 0 > 0.14167)\n Predict: 17.627867328749687\n Else (feature 0 > 0.144655)\n If (feature 12 <= 11.16)\n Predict: 2.1695938843742395\n Else (feature 12 > 11.16)\n Predict: -1.1907031353308928\n Else (feature 10 > 16.5)\n If (feature 0 <= 44.39885)\n If (feature 12 <= 25.6)\n Predict: 0.011456230852588673\n Else (feature 12 > 25.6)\n Predict: -2.9709375014878243\n Else (feature 0 > 44.39885)\n If (feature 0 <= 78.4485)\n Predict: -10.826403178847288\n Else (feature 0 > 78.4485)\n Predict: 0.14304126559714803\n Tree 3 (weight 0.1):\n If (feature 9 <= 222.5)\n If (feature 11 <= 384.5)\n If (feature 0 <= 0.073735)\n If (feature 0 <= 0.017435)\n Predict: 0.11885714285714499\n Else (feature 0 > 0.017435)\n If (feature 0 <= 0.061399999999999996)\n Predict: -0.4960000000000093\n Else (feature 0 > 0.061399999999999996)\n Predict: -1.0808888888888788\n Else (feature 0 > 0.073735)\n If (feature 0 <= 0.093385)\n Predict: 3.432727272727277\n Else (feature 0 > 0.093385)\n Predict: 3.808\n Else (feature 11 > 384.5)\n If (feature 10 <= 17.85)\n If (feature 11 <= 387.245)\n Predict: 10.823111111111125\n Else (feature 11 > 387.245)\n If (feature 11 <= 391.115)\n Predict: 7.3739396825396994\n Else (feature 11 > 391.115)\n Predict: 4.740577142857136\n Else (feature 10 > 17.85)\n If (feature 5 <= 7.254)\n If (feature 12 <= 2.95)\n Predict: -0.04202425925926434\n Else (feature 12 > 2.95)\n Predict: 3.2930348941798937\n Else (feature 5 > 7.254)\n Predict: -1.619092592592608\n Else (feature 9 > 222.5)\n If (feature 0 <= 0.066295)\n If (feature 6 <= 79.05000000000001)\n If (feature 2 <= 6.145)\n If (feature 12 <= 8.434999999999999)\n Predict: -0.7547205321342894\n Else (feature 12 > 8.434999999999999)\n Predict: -3.360123787453107\n Else (feature 2 > 6.145)\n If (feature 4 <= 0.5615)\n Predict: 2.4789190179102034\n Else (feature 4 > 0.5615)\n Predict: -0.5367129629629517\n Else (feature 6 > 79.05000000000001)\n If (feature 0 <= 0.056975)\n If (feature 0 <= 0.047799999999999995)\n Predict: -11.832888888888878\n Else (feature 0 > 0.047799999999999995)\n Predict: -12.016000000000005\n Else (feature 0 > 0.056975)\n Predict: -0.7655669875223552\n Else (feature 0 > 0.066295)\n If (feature 1 <= 31.5)\n If (feature 12 <= 29.615000000000002)\n If (feature 0 <= 44.39885)\n Predict: 0.12864180860415136\n Else (feature 0 > 44.39885)\n Predict: -5.735937357892648\n Else (feature 12 > 29.615000000000002)\n If (feature 12 <= 34.215)\n Predict: -4.8191539760242295\n Else (feature 12 > 34.215)\n Predict: 1.8902590689250165\n Else (feature 1 > 31.5)\n If (feature 0 <= 0.073735)\n If (feature 0 <= 0.07082)\n Predict: 0.016857974195737313\n Else (feature 0 > 0.07082)\n Predict: -4.687402279581526\n Else (feature 0 > 0.073735)\n If (feature 0 <= 0.092825)\n Predict: 7.189752204031103\n Else (feature 0 > 0.092825)\n Predict: 0.27584532687182595\n Tree 4 (weight 0.1):\n If (feature 9 <= 222.5)\n If (feature 5 <= 6.981)\n If (feature 9 <= 190.5)\n If (feature 0 <= 0.073735)\n If (feature 0 <= 0.06902)\n Predict: 0.2768227010582116\n Else (feature 0 > 0.06902)\n Predict: -0.8647111111111059\n Else (feature 0 > 0.073735)\n If (feature 7 <= 1.99525)\n Predict: 3.0281279920634923\n Else (feature 7 > 1.99525)\n Predict: 2.7461818181818245\n Else (feature 9 > 190.5)\n If (feature 5 <= 6.432)\n If (feature 11 <= 387.245)\n Predict: 8.658488888888897\n Else (feature 11 > 387.245)\n Predict: 6.012919791005299\n Else (feature 5 > 6.432)\n If (feature 5 <= 6.617)\n Predict: 2.1726793121693184\n Else (feature 5 > 6.617)\n Predict: 5.220070285714279\n Else (feature 5 > 6.981)\n If (feature 6 <= 54.25)\n If (feature 12 <= 3.0599999999999996)\n If (feature 4 <= 0.44395)\n Predict: -0.39680000000001314\n Else (feature 4 > 0.44395)\n Predict: -0.033619407407414315\n Else (feature 12 > 3.0599999999999996)\n If (feature 10 <= 17.85)\n Predict: 3.1397131428571328\n Else (feature 10 > 17.85)\n Predict: 3.5979872804232755\n Else (feature 6 > 54.25)\n If (feature 0 <= 0.01958)\n Predict: -0.45269957142858175\n Else (feature 0 > 0.01958)\n Predict: -1.2952740740740865\n Else (feature 9 > 222.5)\n If (feature 10 <= 20.95)\n If (feature 11 <= 76.065)\n If (feature 7 <= 2.7811500000000002)\n If (feature 0 <= 44.39885)\n Predict: -2.4802737649228703\n Else (feature 0 > 44.39885)\n Predict: -8.83337951594374\n Else (feature 7 > 2.7811500000000002)\n Predict: 6.381688324372469\n Else (feature 11 > 76.065)\n If (feature 7 <= 6.0113)\n If (feature 7 <= 5.8793)\n Predict: 0.32326627584742224\n Else (feature 7 > 5.8793)\n Predict: 6.2005228368849\n Else (feature 7 > 6.0113)\n If (feature 7 <= 6.4619)\n Predict: -3.5517882905481666\n Else (feature 7 > 6.4619)\n Predict: -0.27900513352826944\n Else (feature 10 > 20.95)\n If (feature 9 <= 334.5)\n If (feature 11 <= 311.205)\n If (feature 0 <= 0.98931)\n Predict: -2.0183116756275297\n Else (feature 0 > 0.98931)\n Predict: 0.11326103093984052\n Else (feature 11 > 311.205)\n If (feature 7 <= 2.292)\n Predict: -0.5209119801941249\n Else (feature 7 > 2.292)\n Predict: -5.440095965520786\n Else (feature 9 > 334.5)\n If (feature 6 <= 97.95)\n If (feature 7 <= 1.9675500000000001)\n Predict: 5.731688324372467\n Else (feature 7 > 1.9675500000000001)\n Predict: 0.03669574508456819\n Else (feature 6 > 97.95)\n If (feature 0 <= 0.34473)\n Predict: -2.3534968608127103\n Else (feature 0 > 0.34473)\n Predict: 0.256507765941451\n Tree 5 (weight 0.1):\n If (feature 9 <= 222.5)\n If (feature 11 <= 384.5)\n If (feature 5 <= 5.962)\n If (feature 0 <= 0.093385)\n Predict: 2.1969454545454568\n Else (feature 0 > 0.093385)\n Predict: 2.440774401587298\n Else (feature 5 > 5.962)\n If (feature 0 <= 0.061399999999999996)\n If (feature 0 <= 0.017435)\n Predict: 0.039721174074074383\n Else (feature 0 > 0.017435)\n Predict: -0.31744000000000483\n Else (feature 0 > 0.061399999999999996)\n Predict: -0.6917688888888875\n Else (feature 11 > 384.5)\n If (feature 10 <= 17.85)\n If (feature 11 <= 387.245)\n Predict: 6.926791111111115\n Else (feature 11 > 387.245)\n If (feature 0 <= 0.10046)\n Predict: 2.964802750793646\n Else (feature 0 > 0.10046)\n Predict: 5.056413371428562\n Else (feature 10 > 17.85)\n If (feature 6 <= 58.75)\n If (feature 12 <= 2.95)\n Predict: -0.026895525925937136\n Else (feature 12 > 2.95)\n Predict: 3.0872411435185185\n Else (feature 6 > 58.75)\n If (feature 0 <= 0.07689)\n Predict: -0.3317279229276906\n Else (feature 0 > 0.07689)\n Predict: 2.4042303857142855\n Else (feature 9 > 222.5)\n If (feature 5 <= 5.805)\n If (feature 2 <= 10.3)\n If (feature 4 <= 0.5485)\n If (feature 0 <= 0.250895)\n Predict: -3.1635009677233725\n Else (feature 0 > 0.250895)\n Predict: -7.8820278297625475\n Else (feature 4 > 0.5485)\n If (feature 0 <= 0.17841)\n Predict: -6.956755733431201\n Else (feature 0 > 0.17841)\n Predict: 3.3950359351191643\n Else (feature 2 > 10.3)\n If (feature 0 <= 33.15885)\n If (feature 3 <= 0.5)\n Predict: 0.401965025792618\n Else (feature 3 > 0.5)\n Predict: -4.74877859977805\n Else (feature 0 > 33.15885)\n If (feature 4 <= 0.601)\n Predict: -7.066703612754992\n Else (feature 4 > 0.601)\n Predict: -6.25914388222435\n Else (feature 5 > 5.805)\n If (feature 5 <= 6.5315)\n If (feature 10 <= 19.4)\n If (feature 0 <= 0.13452)\n Predict: 0.3952963605900748\n Else (feature 0 > 0.13452)\n Predict: 2.574950112766367\n Else (feature 10 > 19.4)\n If (feature 4 <= 0.7155)\n Predict: -0.8148164548240977\n Else (feature 4 > 0.7155)\n Predict: 2.9275888891108086\n Else (feature 5 > 6.5315)\n If (feature 6 <= 99.65)\n If (feature 6 <= 81.05)\n Predict: -0.07498697788956249\n Else (feature 6 > 81.05)\n Predict: -2.1979781203375137\n Else (feature 6 > 99.65)\n If (feature 7 <= 1.89965)\n Predict: 5.260716492558373\n Else (feature 7 > 1.89965)\n Predict: 7.465346122188045\n Tree 6 (weight 0.1):\n If (feature 9 <= 278.0)\n If (feature 9 <= 276.5)\n If (feature 10 <= 19.4)\n If (feature 7 <= 2.0433000000000003)\n If (feature 6 <= 88.45)\n Predict: 1.15550561394767\n Else (feature 6 > 88.45)\n Predict: 4.708445432423211\n Else (feature 7 > 2.0433000000000003)\n If (feature 0 <= 0.52036)\n Predict: 0.7130720620635418\n Else (feature 0 > 0.52036)\n Predict: -2.66578376749563\n Else (feature 10 > 19.4)\n If (feature 7 <= 2.292)\n If (feature 0 <= 0.045520000000000005)\n Predict: -0.16222468336672335\n Else (feature 0 > 0.045520000000000005)\n Predict: -0.0686755699115622\n Else (feature 7 > 2.292)\n If (feature 7 <= 2.5050999999999997)\n Predict: -7.271531261267507\n Else (feature 7 > 2.5050999999999997)\n Predict: -2.365632624725267\n Else (feature 9 > 276.5)\n If (feature 5 <= 6.338)\n If (feature 5 <= 5.893)\n If (feature 5 <= 5.433)\n Predict: 5.95933000787835\n Else (feature 5 > 5.433)\n Predict: 2.3768236201841546\n Else (feature 5 > 5.893)\n If (feature 0 <= 0.20274)\n Predict: 6.639365355126664\n Else (feature 0 > 0.20274)\n Predict: 5.439365355126661\n Else (feature 5 > 6.338)\n Predict: 12.839365355126667\n Else (feature 9 > 278.0)\n If (feature 8 <= 4.5)\n If (feature 5 <= 5.1240000000000006)\n If (feature 7 <= 1.43945)\n Predict: 0.37748239376290016\n Else (feature 7 > 1.43945)\n If (feature 0 <= 0.21081)\n Predict: -10.89398669922845\n Else (feature 0 > 0.21081)\n Predict: -8.201378330519326\n Else (feature 5 > 5.1240000000000006)\n If (feature 2 <= 9.955)\n If (feature 4 <= 0.4095)\n Predict: 0.7041712931110347\n Else (feature 4 > 0.4095)\n Predict: -2.5771774884130507\n Else (feature 2 > 9.955)\n If (feature 7 <= 1.9675500000000001)\n Predict: 2.283710487940413\n Else (feature 7 > 1.9675500000000001)\n Predict: -0.013006042777066185\n Else (feature 8 > 4.5)\n If (feature 4 <= 0.4285)\n If (feature 5 <= 6.8765)\n If (feature 11 <= 375.02)\n Predict: 0.48114159508992554\n Else (feature 11 > 375.02)\n Predict: -2.396913307201946\n Else (feature 5 > 6.8765)\n If (feature 0 <= 0.055815000000000003)\n Predict: -7.304228548773892\n Else (feature 0 > 0.055815000000000003)\n Predict: -7.513018038250593\n Else (feature 4 > 0.4285)\n If (feature 0 <= 13.95715)\n If (feature 6 <= 99.65)\n Predict: 0.3069503130139349\n Else (feature 6 > 99.65)\n Predict: 2.288688314349466\n Else (feature 0 > 13.95715)\n If (feature 5 <= 5.2745)\n Predict: 0.7410330992604349\n Else (feature 5 > 5.2745)\n Predict: -2.229593597494118\n Tree 7 (weight 0.1):\n If (feature 12 <= 25.6)\n If (feature 6 <= 91.15)\n If (feature 6 <= 81.44999999999999)\n If (feature 5 <= 5.744)\n If (feature 12 <= 17.175)\n Predict: -3.320060714869715\n Else (feature 12 > 17.175)\n Predict: 3.5028470169421766\n Else (feature 5 > 5.744)\n If (feature 7 <= 4.39775)\n Predict: 1.1071921647485456\n Else (feature 7 > 4.39775)\n Predict: -0.18550536381572055\n Else (feature 6 > 81.44999999999999)\n If (feature 2 <= 12.379999999999999)\n If (feature 5 <= 6.2695)\n Predict: -1.275907177211928\n Else (feature 5 > 6.2695)\n Predict: -5.279046040684461\n Else (feature 2 > 12.379999999999999)\n If (feature 4 <= 0.8205)\n Predict: 0.6954676497219612\n Else (feature 4 > 0.8205)\n Predict: -8.314986443582406\n Else (feature 6 > 91.15)\n If (feature 6 <= 93.35)\n If (feature 12 <= 9.845)\n If (feature 12 <= 5.945)\n Predict: 5.170017055807751\n Else (feature 12 > 5.945)\n Predict: 6.064535274327351\n Else (feature 12 > 9.845)\n If (feature 11 <= 395.13)\n Predict: 1.8069355807988536\n Else (feature 11 > 395.13)\n Predict: 4.964703894604737\n Else (feature 6 > 93.35)\n If (feature 11 <= 396.22)\n If (feature 7 <= 2.1113999999999997)\n Predict: 0.6851477213780657\n Else (feature 7 > 2.1113999999999997)\n Predict: -1.7623174680183287\n Else (feature 11 > 396.22)\n If (feature 7 <= 2.85435)\n Predict: 1.337163682891686\n Else (feature 7 > 2.85435)\n Predict: 12.26008084874588\n Else (feature 12 > 25.6)\n If (feature 0 <= 0.21081)\n Predict: -8.715189359382759\n Else (feature 0 > 0.21081)\n If (feature 4 <= 0.6695)\n If (feature 2 <= 8.005)\n If (feature 0 <= 0.211425)\n Predict: -0.7890551389846365\n Else (feature 0 > 0.211425)\n Predict: -3.5054295870130368\n Else (feature 2 > 8.005)\n If (feature 0 <= 0.29386999999999996)\n Predict: 4.767464006302681\n Else (feature 0 > 0.29386999999999996)\n Predict: 1.0501602278939979\n Else (feature 4 > 0.6695)\n If (feature 4 <= 0.6964999999999999)\n If (feature 4 <= 0.6859999999999999)\n Predict: -3.248901056877366\n Else (feature 4 > 0.6859999999999999)\n Predict: -6.2531456531199305\n Else (feature 4 > 0.6964999999999999)\n If (feature 12 <= 28.665)\n Predict: 0.03562191628698663\n Else (feature 12 > 28.665)\n Predict: -2.028197524715216\n Tree 8 (weight 0.1):\n If (feature 5 <= 6.0489999999999995)\n If (feature 8 <= 1.5)\n If (feature 0 <= 0.047799999999999995)\n Predict: -6.982460807738331\n Else (feature 0 > 0.047799999999999995)\n Predict: -3.8545324717762455\n Else (feature 8 > 1.5)\n If (feature 7 <= 6.8166)\n If (feature 6 <= 30.950000000000003)\n If (feature 5 <= 5.962)\n Predict: 1.1222983084683378\n Else (feature 5 > 5.962)\n Predict: 5.369424707282956\n Else (feature 6 > 30.950000000000003)\n If (feature 0 <= 33.15885)\n Predict: -0.3731076530376398\n Else (feature 0 > 33.15885)\n Predict: -5.021449822768528\n Else (feature 7 > 6.8166)\n If (feature 0 <= 0.014305)\n Predict: -0.5254317314006087\n Else (feature 0 > 0.014305)\n If (feature 0 <= 0.031315)\n Predict: -4.393203007649738\n Else (feature 0 > 0.031315)\n Predict: -2.9766064790346163\n Else (feature 5 > 6.0489999999999995)\n If (feature 5 <= 6.4475)\n If (feature 7 <= 1.57725)\n If (feature 12 <= 21.945)\n If (feature 0 <= 14.28495)\n Predict: -4.858182323578381\n Else (feature 0 > 14.28495)\n Predict: -5.023233676730207\n Else (feature 12 > 21.945)\n If (feature 2 <= 18.84)\n Predict: 0.646363130967071\n Else (feature 2 > 18.84)\n Predict: -2.1220761721811456\n Else (feature 7 > 1.57725)\n If (feature 12 <= 7.665)\n If (feature 6 <= 54.349999999999994)\n Predict: -0.6231886171066402\n Else (feature 6 > 54.349999999999994)\n Predict: 2.317466643656868\n Else (feature 12 > 7.665)\n If (feature 6 <= 52.849999999999994)\n Predict: 3.6830840217735017\n Else (feature 6 > 52.849999999999994)\n Predict: 1.0313923007147614\n Else (feature 5 > 6.4475)\n If (feature 6 <= 7.3)\n If (feature 0 <= 0.08243)\n Predict: -7.220593045817807\n Else (feature 0 > 0.08243)\n Predict: -5.477295949989617\n Else (feature 6 > 7.3)\n If (feature 9 <= 267.5)\n If (feature 0 <= 0.667655)\n Predict: 1.4004328464203892\n Else (feature 0 > 0.667655)\n Predict: -2.9350818940033356\n Else (feature 9 > 267.5)\n If (feature 9 <= 271.5)\n Predict: -8.356699255694629\n Else (feature 9 > 271.5)\n Predict: -0.4447453001137141\n Tree 9 (weight 0.1):\n If (feature 11 <= 49.685)\n If (feature 7 <= 2.7811500000000002)\n If (feature 11 <= 21.79)\n If (feature 5 <= 5.939)\n If (feature 11 <= 3.575)\n Predict: -4.253842173525095\n Else (feature 11 > 3.575)\n Predict: -1.6149885607290457\n Else (feature 5 > 5.939)\n If (feature 7 <= 1.82005)\n Predict: -2.325510009978615\n Else (feature 7 > 1.82005)\n Predict: 1.7295427220397066\n Else (feature 11 > 21.79)\n If (feature 7 <= 1.8468)\n If (feature 0 <= 11.8817)\n Predict: -2.4178056292614727\n Else (feature 0 > 11.8817)\n Predict: -2.9900610811453827\n Else (feature 7 > 1.8468)\n If (feature 0 <= 0.671855)\n Predict: -5.133911913987443\n Else (feature 0 > 0.671855)\n Predict: -4.97962180874433\n Else (feature 7 > 2.7811500000000002)\n Predict: 5.142451888523141\n Else (feature 11 > 49.685)\n If (feature 6 <= 91.15)\n If (feature 6 <= 82.25)\n If (feature 5 <= 5.8565000000000005)\n If (feature 9 <= 278.0)\n Predict: 0.9122644611699583\n Else (feature 9 > 278.0)\n Predict: -2.337856824438874\n Else (feature 5 > 5.8565000000000005)\n If (feature 0 <= 0.13452)\n Predict: -0.11481683541459015\n Else (feature 0 > 0.13452)\n Predict: 1.42995156443834\n Else (feature 6 > 82.25)\n If (feature 2 <= 10.3)\n If (feature 5 <= 6.5785)\n Predict: -1.3306023666477411\n Else (feature 5 > 6.5785)\n Predict: -5.893719877681628\n Else (feature 2 > 10.3)\n If (feature 5 <= 5.805)\n Predict: -3.3441200168383673\n Else (feature 5 > 5.805)\n Predict: 0.29265138804697466\n Else (feature 6 > 91.15)\n If (feature 6 <= 93.95)\n If (feature 4 <= 0.4555)\n Predict: -2.4161829341795595\n Else (feature 4 > 0.4555)\n If (feature 11 <= 396.22)\n Predict: 2.1824628340558956\n Else (feature 11 > 396.22)\n Predict: 4.5823287913978445\n Else (feature 6 > 93.95)\n If (feature 2 <= 26.695)\n If (feature 11 <= 331.065)\n Predict: 1.8077171489535089\n Else (feature 11 > 331.065)\n Predict: -0.2576760645770411\n Else (feature 2 > 26.695)\n Predict: -6.89752995689868\n Tree 10 (weight 0.1):\n If (feature 7 <= 7.82715)\n If (feature 6 <= 32.7)\n If (feature 6 <= 32.25)\n If (feature 6 <= 4.45)\n Predict: -4.358873392908777\n Else (feature 6 > 4.45)\n If (feature 9 <= 279.5)\n Predict: 2.614198090391087\n Else (feature 9 > 279.5)\n Predict: 0.7006287056183635\n Else (feature 6 > 32.25)\n Predict: 9.027446733909251\n Else (feature 6 > 32.7)\n If (feature 6 <= 37.95)\n If (feature 6 <= 34.150000000000006)\n If (feature 0 <= 0.04062)\n Predict: 1.3039506924727107\n Else (feature 0 > 0.04062)\n Predict: -2.2779657484225453\n Else (feature 6 > 34.150000000000006)\n If (feature 5 <= 4.9864999999999995)\n Predict: -5.354897625946208\n Else (feature 5 > 4.9864999999999995)\n Predict: -3.5755201845986213\n Else (feature 6 > 37.95)\n If (feature 2 <= 4.675000000000001)\n If (feature 11 <= 395.61)\n Predict: 1.8174111387634193\n Else (feature 11 > 395.61)\n Predict: -1.3257871884403953\n Else (feature 2 > 4.675000000000001)\n If (feature 2 <= 6.145)\n Predict: -1.9644112188634717\n Else (feature 2 > 6.145)\n Predict: -0.07283234335910939\n Else (feature 7 > 7.82715)\n If (feature 6 <= 19.65)\n If (feature 11 <= 386.215)\n If (feature 0 <= 0.055815000000000003)\n Predict: -5.694369339150313\n Else (feature 0 > 0.055815000000000003)\n Predict: -5.753511069571317\n Else (feature 11 > 386.215)\n If (feature 0 <= 0.073735)\n Predict: -2.6023588357419882\n Else (feature 0 > 0.073735)\n Predict: -0.6426342986827223\n Else (feature 6 > 19.65)\n If (feature 0 <= 0.197295)\n If (feature 2 <= 2.245)\n If (feature 12 <= 5.9)\n Predict: 1.1169952379078545\n Else (feature 12 > 5.9)\n Predict: -0.9402811667532577\n Else (feature 2 > 2.245)\n If (feature 11 <= 376.355)\n Predict: -2.7567381258684733\n Else (feature 11 > 376.355)\n Predict: -3.311508175430756\n Else (feature 0 > 0.197295)\n Predict: 4.312923529919779\n Tree 11 (weight 0.1):\n If (feature 12 <= 25.6)\n If (feature 7 <= 2.1084)\n If (feature 11 <= 64.83500000000001)\n If (feature 7 <= 1.96955)\n If (feature 7 <= 1.7997)\n Predict: -2.6972634408937974\n Else (feature 7 > 1.7997)\n Predict: 0.14279290471458594\n Else (feature 7 > 1.96955)\n If (feature 7 <= 2.00415)\n Predict: -4.030847020420886\n Else (feature 7 > 2.00415)\n Predict: -0.9999633481263857\n Else (feature 11 > 64.83500000000001)\n If (feature 7 <= 1.7616)\n If (feature 4 <= 0.601)\n Predict: 6.066929257284194\n Else (feature 4 > 0.601)\n Predict: -0.24228463848571358\n Else (feature 7 > 1.7616)\n If (feature 0 <= 7.44656)\n Predict: 0.9420205838564903\n Else (feature 0 > 7.44656)\n Predict: 4.400427831266784\n Else (feature 7 > 2.1084)\n If (feature 5 <= 5.583500000000001)\n If (feature 2 <= 8.350000000000001)\n Predict: -4.648295016120127\n Else (feature 2 > 8.350000000000001)\n If (feature 0 <= 0.17841)\n Predict: -4.406023142206678\n Else (feature 0 > 0.17841)\n Predict: -4.2839181007569636\n Else (feature 5 > 5.583500000000001)\n If (feature 10 <= 17.700000000000003)\n If (feature 7 <= 4.12435)\n Predict: 1.5450263343381379\n Else (feature 7 > 4.12435)\n Predict: -0.0869769224233405\n Else (feature 10 > 17.700000000000003)\n If (feature 6 <= 58.75)\n Predict: 0.34678815892429987\n Else (feature 6 > 58.75)\n Predict: -0.8844081231988755\n Else (feature 12 > 25.6)\n If (feature 0 <= 24.597749999999998)\n If (feature 0 <= 0.21081)\n Predict: -5.503457496847119\n Else (feature 0 > 0.21081)\n If (feature 4 <= 0.6695)\n If (feature 0 <= 0.26139)\n Predict: -1.5770706782043096\n Else (feature 0 > 0.26139)\n Predict: 1.294019817054604\n Else (feature 4 > 0.6695)\n If (feature 0 <= 24.021)\n Predict: -1.4906681285539272\n Else (feature 0 > 24.021)\n Predict: 1.9018463529311447\n Else (feature 0 > 24.597749999999998)\n Predict: -5.802918497713218\n Tree 12 (weight 0.1):\n If (feature 9 <= 222.5)\n If (feature 5 <= 5.606)\n Predict: 4.370315661525829\n Else (feature 5 > 5.606)\n If (feature 12 <= 3.0599999999999996)\n If (feature 0 <= 0.03402)\n Predict: -0.8169928221290093\n Else (feature 0 > 0.03402)\n Predict: -0.9626753851187289\n Else (feature 12 > 3.0599999999999996)\n If (feature 1 <= 20.5)\n If (feature 6 <= 69.65)\n Predict: 2.2087344820636696\n Else (feature 6 > 69.65)\n Predict: 0.6208554044628372\n Else (feature 1 > 20.5)\n If (feature 0 <= 0.05538)\n Predict: 0.4043521308355871\n Else (feature 0 > 0.05538)\n Predict: -1.1490106599094645\n Else (feature 9 > 222.5)\n If (feature 0 <= 0.056975)\n If (feature 6 <= 79.05000000000001)\n If (feature 5 <= 7.33)\n If (feature 12 <= 4.640000000000001)\n Predict: 2.452971761066968\n Else (feature 12 > 4.640000000000001)\n Predict: -0.7079601424865758\n Else (feature 5 > 7.33)\n If (feature 1 <= 86.25)\n Predict: -6.307953276457482\n Else (feature 1 > 86.25)\n Predict: -3.079809037816233\n Else (feature 6 > 79.05000000000001)\n If (feature 0 <= 0.047799999999999995)\n Predict: -5.371557185796146\n Else (feature 0 > 0.047799999999999995)\n Predict: -5.0325399463746905\n Else (feature 0 > 0.056975)\n If (feature 1 <= 31.5)\n If (feature 1 <= 23.5)\n If (feature 12 <= 5.359999999999999)\n Predict: -1.110470806791995\n Else (feature 12 > 5.359999999999999)\n Predict: 0.1210974661428036\n Else (feature 1 > 23.5)\n If (feature 5 <= 6.457000000000001)\n Predict: -1.0490978909017956\n Else (feature 5 > 6.457000000000001)\n Predict: -4.662733775579177\n Else (feature 1 > 31.5)\n If (feature 0 <= 0.09673999999999999)\n If (feature 0 <= 0.073735)\n Predict: 0.14726300049300356\n Else (feature 0 > 0.073735)\n Predict: 5.02570862138245\n Else (feature 0 > 0.09673999999999999)\n If (feature 0 <= 0.105405)\n Predict: -0.56828106468231\n Else (feature 0 > 0.105405)\n Predict: -1.5230738701431932\n Tree 13 (weight 0.1):\n If (feature 0 <= 13.95715)\n If (feature 6 <= 99.1)\n If (feature 5 <= 5.2745)\n If (feature 5 <= 4.9715)\n If (feature 6 <= 95.85)\n Predict: 1.293525272126626\n Else (feature 6 > 95.85)\n Predict: -3.7481916524098615\n Else (feature 5 > 4.9715)\n If (feature 0 <= 1.1669999999999998)\n Predict: -5.148362604228284\n Else (feature 0 > 1.1669999999999998)\n Predict: -2.9024850080912046\n Else (feature 5 > 5.2745)\n If (feature 10 <= 17.700000000000003)\n If (feature 5 <= 6.888)\n Predict: 0.9787830524469152\n Else (feature 5 > 6.888)\n Predict: -0.4498298999361705\n Else (feature 10 > 17.700000000000003)\n If (feature 5 <= 6.4475)\n Predict: 0.05020970421684005\n Else (feature 5 > 6.4475)\n Predict: -0.9177171759621962\n Else (feature 6 > 99.1)\n If (feature 0 <= 5.049995)\n If (feature 2 <= 3.9850000000000003)\n If (feature 7 <= 1.89965)\n Predict: 2.325197369165224\n Else (feature 7 > 1.89965)\n Predict: 4.088901072868964\n Else (feature 2 > 3.9850000000000003)\n If (feature 12 <= 13.335)\n Predict: -1.8180249881654422\n Else (feature 12 > 13.335)\n Predict: 0.1363865238490464\n Else (feature 0 > 5.049995)\n If (feature 4 <= 0.7155)\n If (feature 7 <= 1.3034)\n Predict: 0.2993875151646783\n Else (feature 7 > 1.3034)\n Predict: 2.979821613476164\n Else (feature 4 > 0.7155)\n If (feature 0 <= 10.014420000000001)\n Predict: 9.270829099696702\n Else (feature 0 > 10.014420000000001)\n Predict: 11.1625229368249\n Else (feature 0 > 13.95715)\n If (feature 11 <= 363.225)\n If (feature 11 <= 13.25)\n If (feature 4 <= 0.601)\n Predict: -2.959615652100041\n Else (feature 4 > 0.601)\n Predict: -1.607663485867917\n Else (feature 11 > 13.25)\n If (feature 12 <= 23.255)\n If (feature 4 <= 0.601)\n Predict: 4.829323912598795\n Else (feature 4 > 0.601)\n Predict: 2.3392114294970234\n Else (feature 12 > 23.255)\n If (feature 0 <= 18.2914)\n Predict: -2.1035682637622575\n Else (feature 0 > 18.2914)\n Predict: -0.3099442491870974\n Else (feature 11 > 363.225)\n If (feature 5 <= 4.9045)\n If (feature 4 <= 0.6635)\n Predict: -2.295754665541118\n Else (feature 4 > 0.6635)\n If (feature 0 <= 20.400550000000003)\n Predict: 1.3681372364515454\n Else (feature 0 > 20.400550000000003)\n Predict: 1.497257589116355\n Else (feature 5 > 4.9045)\n If (feature 6 <= 98.15)\n If (feature 5 <= 5.9265)\n Predict: -2.0655104766527264\n Else (feature 5 > 5.9265)\n Predict: 0.2616594282831481\n Else (feature 6 > 98.15)\n If (feature 7 <= 1.57725)\n Predict: -3.7760838060273323\n Else (feature 7 > 1.57725)\n Predict: -4.666554291399136\n Tree 14 (weight 0.1):\n If (feature 7 <= 7.82715)\n If (feature 6 <= 32.7)\n If (feature 6 <= 32.25)\n If (feature 6 <= 4.45)\n Predict: -3.15081874956104\n Else (feature 6 > 4.45)\n If (feature 9 <= 279.5)\n Predict: 1.8718470502425975\n Else (feature 9 > 279.5)\n Predict: 0.4408384445495334\n Else (feature 6 > 32.25)\n Predict: 7.118338321270613\n Else (feature 6 > 32.7)\n If (feature 7 <= 3.79325)\n If (feature 1 <= 33.5)\n If (feature 6 <= 42.150000000000006)\n Predict: -2.1982130713631705\n Else (feature 6 > 42.150000000000006)\n Predict: 0.12494903997137141\n Else (feature 1 > 33.5)\n Predict: 6.833079198670049\n Else (feature 7 > 3.79325)\n If (feature 10 <= 15.55)\n If (feature 0 <= 0.211425)\n Predict: 1.4822015195788374\n Else (feature 0 > 0.211425)\n Predict: -5.520250915926489\n Else (feature 10 > 15.55)\n If (feature 7 <= 7.3582)\n Predict: -0.9892649317648092\n Else (feature 7 > 7.3582)\n Predict: 5.002186707835683\n Else (feature 7 > 7.82715)\n If (feature 0 <= 0.197295)\n If (feature 6 <= 11.95)\n If (feature 0 <= 0.055815000000000003)\n Predict: -4.306542078351036\n Else (feature 0 > 0.055815000000000003)\n Predict: -4.266528890891074\n Else (feature 6 > 11.95)\n If (feature 2 <= 1.315)\n If (feature 0 <= 0.02942)\n Predict: 2.5972288627304962\n Else (feature 0 > 0.02942)\n Predict: -0.1133317430609182\n Else (feature 2 > 1.315)\n If (feature 5 <= 6.2275)\n Predict: -2.120421371300273\n Else (feature 5 > 6.2275)\n Predict: -0.8167285587042571\n Else (feature 0 > 0.197295)\n If (feature 0 <= 0.339945)\n Predict: 3.3467197580790327\n Else (feature 0 > 0.339945)\n Predict: -0.17782747418020506\n Tree 15 (weight 0.1):\n If (feature 5 <= 8.2625)\n If (feature 5 <= 7.3454999999999995)\n If (feature 5 <= 7.121)\n If (feature 5 <= 7.07)\n If (feature 9 <= 278.0)\n Predict: 0.5420057841728215\n Else (feature 9 > 278.0)\n Predict: -0.13160621983973442\n Else (feature 5 > 7.07)\n If (feature 0 <= 0.047799999999999995)\n Predict: -1.1752525015932136\n Else (feature 0 > 0.047799999999999995)\n Predict: -5.952247739766271\n Else (feature 5 > 7.121)\n If (feature 0 <= 0.667655)\n If (feature 12 <= 3.565)\n Predict: 3.985379704800799\n Else (feature 12 > 3.565)\n Predict: 1.3149099085883877\n Else (feature 0 > 0.667655)\n Predict: -3.10407934425308\n Else (feature 5 > 7.3454999999999995)\n If (feature 5 <= 7.6274999999999995)\n If (feature 10 <= 17.5)\n If (feature 7 <= 1.97915)\n Predict: -0.3815200082560466\n Else (feature 7 > 1.97915)\n Predict: -3.5999876136972304\n Else (feature 10 > 17.5)\n If (feature 1 <= 6.25)\n Predict: 0.7715656829859938\n Else (feature 1 > 6.25)\n Predict: -0.7606549007294063\n Else (feature 5 > 7.6274999999999995)\n If (feature 7 <= 3.51385)\n If (feature 5 <= 7.9845)\n Predict: -0.5697115719597662\n Else (feature 5 > 7.9845)\n Predict: -2.9126335499238536\n Else (feature 7 > 3.51385)\n If (feature 1 <= 6.25)\n Predict: 3.0201032055787493\n Else (feature 1 > 6.25)\n Predict: 0.0492691052257328\n Else (feature 5 > 8.2625)\n If (feature 0 <= 0.31898499999999996)\n Predict: 7.395103205578778\n Else (feature 0 > 0.31898499999999996)\n If (feature 6 <= 67.1)\n Predict: -1.6353754893419392\n Else (feature 6 > 67.1)\n If (feature 0 <= 0.52036)\n Predict: 2.850384341100451\n Else (feature 0 > 0.52036)\n If (feature 2 <= 18.84)\n Predict: 1.0261723179686584\n Else (feature 2 > 18.84)\n Predict: -0.38852642400611614\n Tree 16 (weight 0.1):\n If (feature 0 <= 13.95715)\n If (feature 6 <= 99.65)\n If (feature 5 <= 5.2745)\n If (feature 0 <= 1.1669999999999998)\n If (feature 0 <= 0.21081)\n Predict: -3.395981533886925\n Else (feature 0 > 0.21081)\n Predict: -4.83873576093098\n Else (feature 0 > 1.1669999999999998)\n If (feature 0 <= 2.378955)\n Predict: 1.5944347291384844\n Else (feature 0 > 2.378955)\n Predict: -1.6740085066186063\n Else (feature 5 > 5.2745)\n If (feature 10 <= 15.75)\n If (feature 5 <= 6.954)\n Predict: 1.4343804954364765\n Else (feature 5 > 6.954)\n Predict: -0.31178017862397633\n Else (feature 10 > 15.75)\n If (feature 10 <= 15.95)\n Predict: -3.5823926087783384\n Else (feature 10 > 15.95)\n Predict: -0.06986564407846174\n Else (feature 6 > 99.65)\n If (feature 0 <= 5.049995)\n If (feature 2 <= 3.9850000000000003)\n If (feature 5 <= 6.848)\n Predict: 3.1377298934663287\n Else (feature 5 > 6.848)\n Predict: 1.5721861056202329\n Else (feature 2 > 3.9850000000000003)\n If (feature 0 <= 1.50767)\n Predict: -1.6151239411555807\n Else (feature 0 > 1.50767)\n Predict: 0.1732224967972602\n Else (feature 0 > 5.049995)\n If (feature 4 <= 0.7064999999999999)\n If (feature 7 <= 1.3034)\n Predict: 0.24084144810541552\n Else (feature 7 > 1.3034)\n Predict: 2.8579565474671744\n Else (feature 4 > 0.7064999999999999)\n If (feature 0 <= 10.014420000000001)\n Predict: 7.417994715731034\n Else (feature 0 > 10.014420000000001)\n Predict: 8.931349785433593\n Else (feature 0 > 13.95715)\n If (feature 11 <= 363.225)\n If (feature 11 <= 13.25)\n If (feature 0 <= 16.33605)\n Predict: -1.2847993527206611\n Else (feature 0 > 16.33605)\n Predict: -2.366361085706359\n Else (feature 11 > 13.25)\n If (feature 12 <= 23.255)\n If (feature 4 <= 0.601)\n Predict: 3.864790566052708\n Else (feature 4 > 0.601)\n Predict: 1.8727005795712905\n Else (feature 12 > 23.255)\n If (feature 0 <= 18.2914)\n Predict: -1.6815231750361352\n Else (feature 0 > 18.2914)\n Predict: -0.24662396337600612\n Else (feature 11 > 363.225)\n If (feature 5 <= 4.9045)\n If (feature 4 <= 0.6635)\n Predict: -1.8352722964592232\n Else (feature 4 > 0.6635)\n If (feature 0 <= 20.400550000000003)\n Predict: 1.0958412251349081\n Else (feature 0 > 20.400550000000003)\n Predict: 1.1991375072667552\n Else (feature 5 > 4.9045)\n If (feature 6 <= 96.30000000000001)\n If (feature 5 <= 5.9265)\n Predict: -1.5605067819842908\n Else (feature 5 > 5.9265)\n Predict: 0.21065897860019098\n Else (feature 6 > 96.30000000000001)\n If (feature 12 <= 26.795)\n Predict: -3.197629705922554\n Else (feature 12 > 26.795)\n Predict: -2.0133575988053867\n Tree 17 (weight 0.1):\n If (feature 9 <= 222.5)\n If (feature 11 <= 384.5)\n If (feature 0 <= 0.073735)\n If (feature 0 <= 0.017435)\n Predict: -0.7445726186228967\n Else (feature 0 > 0.017435)\n If (feature 0 <= 0.061399999999999996)\n Predict: -0.9241124173438635\n Else (feature 0 > 0.061399999999999996)\n Predict: -0.8812145649075447\n Else (feature 0 > 0.073735)\n If (feature 0 <= 0.093385)\n Predict: 0.46421529301169073\n Else (feature 0 > 0.093385)\n Predict: 0.5725891281839708\n Else (feature 11 > 384.5)\n If (feature 5 <= 6.432)\n If (feature 2 <= 2.675)\n If (feature 11 <= 387.245)\n Predict: 4.236257674084285\n Else (feature 11 > 387.245)\n Predict: 3.1731006808403244\n Else (feature 2 > 2.675)\n If (feature 0 <= 0.06902)\n Predict: 0.1408900200951564\n Else (feature 0 > 0.06902)\n Predict: 1.1418124029870924\n Else (feature 5 > 6.432)\n If (feature 0 <= 0.05506)\n If (feature 1 <= 33.5)\n Predict: -0.17536688537101006\n Else (feature 1 > 33.5)\n Predict: 0.737957547133945\n Else (feature 0 > 0.05506)\n If (feature 5 <= 7.254)\n Predict: 1.3555151296656447\n Else (feature 5 > 7.254)\n Predict: 0.05748271962553986\n Else (feature 9 > 222.5)\n If (feature 0 <= 0.066295)\n If (feature 6 <= 56.25)\n If (feature 8 <= 6.5)\n If (feature 11 <= 386.485)\n Predict: -1.3134274191142132\n Else (feature 11 > 386.485)\n Predict: 0.2866105189313335\n Else (feature 8 > 6.5)\n Predict: -4.97173483108827\n Else (feature 6 > 56.25)\n If (feature 5 <= 6.981)\n If (feature 9 <= 298.0)\n Predict: -0.707435935367538\n Else (feature 9 > 298.0)\n Predict: -4.029085059531877\n Else (feature 5 > 6.981)\n If (feature 0 <= 0.053309999999999996)\n Predict: -4.747825062997322\n Else (feature 0 > 0.053309999999999996)\n Predict: -3.961906357920462\n Else (feature 0 > 0.066295)\n If (feature 0 <= 0.06653)\n Predict: 6.463895946474551\n Else (feature 0 > 0.06653)\n If (feature 1 <= 31.5)\n If (feature 5 <= 6.6295)\n Predict: 0.11340164321592056\n Else (feature 5 > 6.6295)\n Predict: -0.6137059817812344\n Else (feature 1 > 31.5)\n If (feature 12 <= 7.82)\n Predict: 1.0993872050745301\n Else (feature 12 > 7.82)\n Predict: 6.676713261233729\n Tree 18 (weight 0.1):\n If (feature 5 <= 8.2625)\n If (feature 6 <= 91.15)\n If (feature 6 <= 81.44999999999999)\n If (feature 5 <= 5.964)\n If (feature 10 <= 16.700000000000003)\n Predict: 0.98590196847969\n Else (feature 10 > 16.700000000000003)\n Predict: -0.9506396361890554\n Else (feature 5 > 5.964)\n If (feature 7 <= 3.9677)\n Predict: 1.076789542353181\n Else (feature 7 > 3.9677)\n Predict: -0.03660662425758053\n Else (feature 6 > 81.44999999999999)\n If (feature 4 <= 0.5485)\n If (feature 5 <= 6.5785)\n Predict: -0.8863847363944727\n Else (feature 5 > 6.5785)\n Predict: -5.726661091183939\n Else (feature 4 > 0.5485)\n If (feature 7 <= 2.81265)\n Predict: -0.3970470434365424\n Else (feature 7 > 2.81265)\n Predict: 4.1556895149891275\n Else (feature 6 > 91.15)\n If (feature 6 <= 93.95)\n If (feature 7 <= 3.0793)\n If (feature 12 <= 16.92)\n Predict: 2.622897648771859\n Else (feature 12 > 16.92)\n Predict: 1.0579670306177784\n Else (feature 7 > 3.0793)\n If (feature 0 <= 0.114425)\n Predict: 0.07374236727732608\n Else (feature 0 > 0.114425)\n Predict: -0.9479378111007577\n Else (feature 6 > 93.95)\n If (feature 0 <= 0.144655)\n If (feature 0 <= 0.13452)\n Predict: 0.75872149291488\n Else (feature 0 > 0.13452)\n Predict: 8.885631693529405\n Else (feature 0 > 0.144655)\n If (feature 7 <= 2.1288)\n Predict: 0.09613113687153829\n Else (feature 7 > 2.1288)\n Predict: -1.515044786097363\n Else (feature 5 > 8.2625)\n If (feature 4 <= 0.5055000000000001)\n Predict: 6.052796889634962\n Else (feature 4 > 0.5055000000000001)\n If (feature 12 <= 5.945)\n If (feature 0 <= 0.61312)\n If (feature 6 <= 87.1)\n Predict: 1.2315751135222044\n Else (feature 6 > 87.1)\n Predict: 2.4654047049614007\n Else (feature 0 > 0.61312)\n If (feature 0 <= 1.62073)\n Predict: -0.12572390712385584\n Else (feature 0 > 1.62073)\n Predict: 0.4581892185052894\n Else (feature 12 > 5.945)\n Predict: -1.1232031593925171\n Tree 19 (weight 0.1):\n If (feature 0 <= 13.95715)\n If (feature 6 <= 99.1)\n If (feature 5 <= 5.2745)\n If (feature 0 <= 1.1669999999999998)\n If (feature 2 <= 20.735)\n Predict: -3.814259528700653\n Else (feature 2 > 20.735)\n Predict: -2.7586917831270306\n Else (feature 0 > 1.1669999999999998)\n If (feature 0 <= 2.378955)\n Predict: 1.2336412272932975\n Else (feature 0 > 2.378955)\n Predict: -1.4551131482543633\n Else (feature 5 > 5.2745)\n If (feature 2 <= 10.3)\n If (feature 2 <= 9.795)\n Predict: -0.033814026312272726\n Else (feature 2 > 9.795)\n Predict: -1.3489987711094296\n Else (feature 2 > 10.3)\n If (feature 2 <= 10.7)\n Predict: 2.6250608124416295\n Else (feature 2 > 10.7)\n Predict: 0.12800677364930674\n Else (feature 6 > 99.1)\n If (feature 2 <= 18.84)\n If (feature 4 <= 0.7155)\n If (feature 7 <= 1.3387)\n Predict: 0.12546702488201925\n Else (feature 7 > 1.3387)\n Predict: 1.7265793257872293\n Else (feature 4 > 0.7155)\n If (feature 0 <= 10.014420000000001)\n Predict: 5.892489216567338\n Else (feature 0 > 10.014420000000001)\n Predict: 7.103173272329382\n Else (feature 2 > 18.84)\n If (feature 0 <= 1.50767)\n If (feature 0 <= 1.365415)\n Predict: -0.5531402839955533\n Else (feature 0 > 1.365415)\n Predict: -2.2836390813438143\n Else (feature 0 > 1.50767)\n If (feature 12 <= 26.795)\n Predict: 1.480438054243404\n Else (feature 12 > 26.795)\n Predict: -0.8185717765590965\n Else (feature 0 > 13.95715)\n If (feature 5 <= 5.2745)\n If (feature 11 <= 219.29000000000002)\n Predict: 3.049925896824675\n Else (feature 11 > 219.29000000000002)\n If (feature 4 <= 0.6635)\n Predict: -1.5101243931848707\n Else (feature 4 > 0.6635)\n If (feature 6 <= 91.25)\n Predict: -0.8264683083652944\n Else (feature 6 > 91.25)\n Predict: 0.8760849369431742\n Else (feature 5 > 5.2745)\n If (feature 6 <= 96.30000000000001)\n If (feature 5 <= 5.9265)\n If (feature 4 <= 0.6964999999999999)\n Predict: -1.678013798466657\n Else (feature 4 > 0.6964999999999999)\n Predict: 0.14003375397379259\n Else (feature 5 > 5.9265)\n If (feature 11 <= 363.225)\n Predict: 1.33471524021647\n Else (feature 11 > 363.225)\n Predict: 0.14385062096659595\n Else (feature 6 > 96.30000000000001)\n If (feature 12 <= 26.795)\n If (feature 7 <= 1.57725)\n Predict: -2.300186023906524\n Else (feature 7 > 1.57725)\n Predict: -3.1342926119786174\n Else (feature 12 > 26.795)\n If (feature 0 <= 17.97565)\n Predict: -1.652592635061799\n Else (feature 0 > 17.97565)\n Predict: -1.3871250960464003\n'
In [23]:
model.stages[1].save('BostonGBT')
model_df = spark.read.parquet("BostonGBT/data")
print(model_df.printSchema())
root
|-- treeID: integer (nullable = true)
|-- nodeData: struct (nullable = true)
| |-- id: integer (nullable = true)
| |-- prediction: double (nullable = true)
| |-- impurity: double (nullable = true)
| |-- impurityStats: array (nullable = true)
| | |-- element: double (containsNull = true)
| |-- gain: double (nullable = true)
| |-- leftChild: integer (nullable = true)
| |-- rightChild: integer (nullable = true)
| |-- split: struct (nullable = true)
| | |-- featureIndex: integer (nullable = true)
| | |-- leftCategoriesOrThreshold: array (nullable = true)
| | | |-- element: double (containsNull = true)
| | |-- numCategories: integer (nullable = true)
None
In [24]:
model_df.toJSON().collect()
Out[24]:
['{"treeID":0,"nodeData":{"id":0,"prediction":23.015819209039552,"impurity":87.89652941364231,"impurityStats":[354.0,8147.600000000001,218639.06000000006],"gain":40.484981224398176,"leftChild":1,"rightChild":28,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[6.941],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":1,"prediction":20.08390410958904,"impurity":41.294501196284266,"impurityStats":[292.0,5864.499999999999,129840.04999999992],"gain":17.898934791141883,"leftChild":2,"rightChild":13,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[14.399999999999999],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":2,"prediction":23.469662921348323,"impurity":26.11368640323184,"impurityStats":[178.0,4177.600000000001,102695.10000000003],"gain":10.14308473075324,"leftChild":3,"rightChild":6,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[1.35735],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":3,"prediction":44.475,"impurity":91.57687499999975,"impurityStats":[4.0,177.9,8278.41],"gain":91.57687499999986,"leftChild":4,"rightChild":5,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[8.37969],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":4,"prediction":50.0,"impurity":0.0,"impurityStats":[3.0,150.0,7500.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":5,"prediction":27.900000000000006,"impurity":-1.0,"impurityStats":[1.0,27.900000000000006,778.4099999999999],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":6,"prediction":22.986781609195397,"impurity":14.23252642357007,"impurityStats":[174.0,3999.6999999999994,94416.69000000002],"gain":5.891743642395225,"leftChild":7,"rightChild":10,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[6.543],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":7,"prediction":21.6390977443609,"impurity":7.17426084007019,"impurityStats":[133.0,2877.9999999999995,63231.49999999999],"gain":1.0570257523506053,"leftChild":8,"rightChild":9,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[7.76],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":8,"prediction":23.776,"impurity":2.2794240000000716,"impurityStats":[25.0,594.4,14189.44],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":9,"prediction":21.144444444444435,"impurity":7.005617283950953,"impurityStats":[108.0,2283.599999999999,49042.05999999999],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":10,"prediction":27.35853658536586,"impurity":12.1248661510997,"impurityStats":[41.0,1121.7000000000003,31185.18999999998],"gain":3.6374671318712117,"leftChild":11,"rightChild":12,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[5.6899999999999995],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":11,"prediction":30.00714285714286,"impurity":8.642091836734705,"impurityStats":[14.0,420.1,12726.990000000002],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":12,"prediction":25.985185185185177,"impurity":8.40718792866971,"impurityStats":[27.0,701.5999999999998,18458.199999999997],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":13,"prediction":14.797368421052628,"impurity":19.151484302862357,"impurityStats":[114.0,1686.8999999999996,27144.949999999983],"gain":7.639321467112913,"leftChild":14,"rightChild":21,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[2.0754],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":14,"prediction":12.266129032258062,"impurity":12.16159469302813,"impurityStats":[62.0,760.4999999999999,10082.41],"gain":4.091214213292885,"leftChild":15,"rightChild":18,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[6.340595],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":15,"prediction":14.900000000000002,"impurity":6.959999999999917,"impurityStats":[23.0,342.70000000000005,5266.3099999999995],"gain":2.2041666666666613,"leftChild":16,"rightChild":17,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[15.47],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":16,"prediction":18.733333333333334,"impurity":3.7955555555554383,"impurityStats":[3.0,56.2,1064.1999999999998],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":17,"prediction":14.325,"impurity":4.899874999999929,"impurityStats":[20.0,286.5,4202.109999999999],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":18,"prediction":10.712820512820512,"impurity":8.725220249835566,"impurityStats":[39.0,417.79999999999995,4816.099999999997],"gain":2.0063915785069053,"leftChild":19,"rightChild":20,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[0.675],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":19,"prediction":12.972727272727274,"impurity":3.0819834710743264,"impurityStats":[11.0,142.70000000000002,1885.11],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":20,"prediction":9.825000000000001,"impurity":8.147589285714291,"impurityStats":[28.0,275.1,2930.9900000000007],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":21,"prediction":17.815384615384616,"impurity":10.737840236686399,"impurityStats":[52.0,926.4,17062.54],"gain":4.014777772834655,"leftChild":22,"rightChild":25,"split":{"featureIndex":11,"leftCategoriesOrThreshold":[311.205],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":22,"prediction":14.514285714285718,"impurity":2.874081632652925,"impurityStats":[14.0,203.20000000000005,2989.5399999999995],"gain":1.371700680271975,"leftChild":23,"rightChild":24,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[17.375],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":23,"prediction":15.866666666666665,"impurity":3.31888888888894,"impurityStats":[6.0,95.19999999999999,1530.4199999999998],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":24,"prediction":13.5,"impurity":0.13999999999995794,"impurityStats":[8.0,108.0,1459.1199999999997],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":25,"prediction":19.031578947368427,"impurity":8.141108033240782,"impurityStats":[38.0,723.2000000000003,14073.000000000002],"gain":2.024457315537491,"leftChild":26,"rightChild":27,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[84.55],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":26,"prediction":20.700000000000003,"impurity":3.1462499999998954,"impurityStats":[16.0,331.20000000000005,6906.18],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":27,"prediction":17.818181818181817,"impurity":8.276942148760305,"impurityStats":[22.0,392.0,7166.82],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":28,"prediction":36.8241935483871,"impurity":76.22086628511968,"impurityStats":[62.0,2283.1000000000004,88799.01000000002],"gain":33.70271511081346,"leftChild":29,"rightChild":40,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[7.418],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":29,"prediction":32.21052631578947,"impurity":40.45620498614957,"impurityStats":[38.0,1224.0,40963.02],"gain":18.221731301939492,"leftChild":30,"rightChild":37,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[6.59684],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":30,"prediction":33.21666666666667,"impurity":22.70916666666623,"impurityStats":[36.0,1195.8000000000002,40538.02],"gain":9.093398692809918,"leftChild":31,"rightChild":34,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[1.88595],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":31,"prediction":45.65,"impurity":18.922500000000127,"impurityStats":[2.0,91.3,4205.69],"gain":18.92250000000024,"leftChild":32,"rightChild":33,"split":{"featureIndex":2,"leftCategoriesOrThreshold":[18.84],"numCategories":-1}}}',
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'{"treeID":19,"nodeData":{"id":31,"prediction":-0.8185717765590965,"impurity":0.5689126144666492,"impurityStats":[3.0,-2.4557153296772896,3.716917103537294],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":32,"prediction":-0.7058520443641272,"impurity":2.3913780515132883,"impurityStats":[22.0,-15.528744976010799,63.57131352101874],"gain":0.3594333676518875,"leftChild":33,"rightChild":40,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[5.2745],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":33,"prediction":0.273172460132594,"impurity":2.391872176929087,"impurityStats":[6.0,1.639034760795564,14.798972219423884],"gain":1.542071929636257,"leftChild":34,"rightChild":35,"split":{"featureIndex":11,"leftCategoriesOrThreshold":[219.29000000000002],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":34,"prediction":3.049925896824675,"impurity":0.0,"impurityStats":[1.0,3.049925896824675,9.302047976121798],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":35,"prediction":-0.2821782272058222,"impurity":1.0197602967513961,"impurityStats":[5.0,-1.410891136029111,5.496924243302084],"gain":0.3769629466356609,"leftChild":36,"rightChild":37,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[0.6635],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":36,"prediction":-1.5101243931848707,"impurity":0.0,"impurityStats":[1.0,-1.5101243931848707,2.280475682891974],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":37,"prediction":0.024808314288939926,"impurity":0.803496687644669,"impurityStats":[4.0,0.0992332571557597,3.216448560410111],"gain":0.7246718882775995,"leftChild":38,"rightChild":39,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[91.25],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":38,"prediction":-0.8264683083652944,"impurity":0.15594237922977694,"impurityStats":[2.0,-1.6529366167305888,1.6779844879239365],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":39,"prediction":0.8760849369431742,"impurity":0.0017072195043620342,"impurityStats":[2.0,1.7521698738863485,1.5384640724861753],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":40,"prediction":-1.0729862335503977,"impurity":1.8969718739610184,"impurityStats":[16.0,-17.167779736806363,48.77234130159499],"gain":0.9815896740535175,"leftChild":41,"rightChild":48,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[96.30000000000001],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":41,"prediction":-0.1992250331214945,"impurity":1.3935192952014395,"impurityStats":[9.0,-1.7930252980934505,12.8988891812133],"gain":0.8393144969907086,"leftChild":42,"rightChild":45,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[5.9265],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":42,"prediction":-1.2235019103565445,"impurity":0.7609254518724207,"impurityStats":[4.0,-4.894007641426178,9.031529506074138],"gain":0.6197431693002583,"leftChild":43,"rightChild":44,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[0.6964999999999999],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":43,"prediction":-1.678013798466657,"impurity":0.18824304342954967,"impurityStats":[3.0,-5.034041395399971,9.011920053822145],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":44,"prediction":0.14003375397379259,"impurity":5.065392549852277E-16,"impurityStats":[1.0,0.14003375397379259,0.019609452251993176],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":45,"prediction":0.6201964686665455,"impurity":0.38882827528137914,"impurityStats":[5.0,3.1009823433327277,3.867359675139163],"gain":0.34035804993147567,"leftChild":46,"rightChild":47,"split":{"featureIndex":11,"leftCategoriesOrThreshold":[363.225],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":46,"prediction":1.33471524021647,"impurity":0.02082737772302523,"impurityStats":[2.0,2.66943048043294,3.6045843003782685],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":47,"prediction":0.14385062096659595,"impurity":0.06689879043448892,"impurityStats":[3.0,0.43155186289978786,0.2627753747608925],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":48,"prediction":-2.196393491244702,"impurity":0.30063450595815155,"impurityStats":[7.0,-15.374754438712912,35.87345212038169],"gain":0.18307963990332912,"leftChild":49,"rightChild":52,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[26.795],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":49,"prediction":-2.467007341520943,"impurity":0.15752951104966256,"impurityStats":[5.0,-12.335036707604713,31.21827367083946],"gain":0.1113174080424425,"leftChild":50,"rightChild":51,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[1.57725],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":50,"prediction":-2.300186023906524,"impurity":0.05776512875902462,"impurityStats":[4.0,-9.200744095626096,21.394483493335713],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":51,"prediction":-3.1342926119786174,"impurity":1.7763568394002505E-15,"impurityStats":[1.0,-3.1342926119786174,9.823790177503746],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":52,"prediction":-1.5198588655540997,"impurity":0.017618253567722064,"impurityStats":[2.0,-3.0397177311081993,4.655178449542234],"gain":0.017618253567722175,"leftChild":53,"rightChild":54,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[17.97565],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":53,"prediction":-1.652592635061799,"impurity":0.0,"impurityStats":[1.0,-1.652592635061799,2.7310624174605005],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":54,"prediction":-1.3871250960464003,"impurity":-2.220446049250313E-16,"impurityStats":[1.0,-1.3871250960464003,1.9241160320817352],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}']
In [25]:
predictions = model.transform(vtest_df)
# Evaluate model on test instances and compute test error
predictions.select("prediction","MV","features").show(5)
from pyspark.ml.evaluation import RegressionEvaluator
evaluator = RegressionEvaluator(predictionCol="prediction", \
labelCol="MV",metricName="r2")
print("R Squared (R2) on test data = %g" % evaluator.evaluate(predictions))
+------------------+----+--------------------+
| prediction| MV| features|
+------------------+----+--------------------+
|21.528964254942217|23.6|[0.09178,0.0,4.05...|
|31.139006072368268|32.4|[0.05644,40.0,6.4...|
| 13.98456646285668|13.6|[0.10574,0.0,27.7...|
|24.166545057677336|22.8|[0.09164,0.0,10.8...|
| 18.28798200702338|16.1|[5.09017,0.0,18.1...|
+------------------+----+--------------------+
only showing top 5 rows
R Squared (R2) on test data = 0.835861
In [26]:
rf = RandomForestRegressor(labelCol="MV", maxDepth=5, maxBins=255)
pipeline = Pipeline(stages=[featureIndexer, rf])
model = pipeline.fit(vtrain_df)
In [27]:
print(model.stages[1].getNumTrees)
print(model.stages[1].featureImportances)
20
(13,[0,1,2,3,4,5,6,7,8,9,10,11,12],[0.030119488974281333,0.0029449287569198444,0.06477882208906569,0.004511645239718717,0.05924761354682164,0.3409638351883184,0.013096149600859197,0.07663019081544346,0.02421287386835675,0.01754026418937741,0.0413750254453455,0.006908302233599355,0.31767086005189277])
In [28]:
model.stages[1].toDebugString
Out[28]:
'RandomForestRegressionModel (uid=RandomForestRegressor_4d6c98b4562149379015) with 20 trees\n Tree 0 (weight 1.0):\n If (feature 5 <= 6.941)\n If (feature 12 <= 14.399999999999999)\n If (feature 5 <= 6.543)\n If (feature 7 <= 1.3034)\n Predict: 27.9\n Else (feature 7 > 1.3034)\n If (feature 6 <= 9.350000000000001)\n Predict: 24.48571428571429\n Else (feature 6 > 9.350000000000001)\n Predict: 21.273636363636367\n Else (feature 5 > 6.543)\n If (feature 7 <= 5.4009)\n If (feature 6 <= 41.3)\n Predict: 29.558333333333334\n Else (feature 6 > 41.3)\n Predict: 26.99999999999999\n Else (feature 7 > 5.4009)\n If (feature 6 <= 30.950000000000003)\n Predict: 26.82857142857143\n Else (feature 6 > 30.950000000000003)\n Predict: 23.522222222222226\n Else (feature 12 > 14.399999999999999)\n If (feature 7 <= 2.0754)\n If (feature 0 <= 4.2418499999999995)\n If (feature 7 <= 1.3513000000000002)\n Predict: 12.866666666666667\n Else (feature 7 > 1.3513000000000002)\n Predict: 15.550000000000002\n Else (feature 0 > 4.2418499999999995)\n If (feature 12 <= 23.835)\n Predict: 12.08787878787879\n Else (feature 12 > 23.835)\n Predict: 8.623529411764697\n Else (feature 7 > 2.0754)\n If (feature 12 <= 18.78)\n If (feature 5 <= 6.1145)\n Predict: 19.89285714285714\n Else (feature 5 > 6.1145)\n Predict: 16.16363636363637\n Else (feature 12 > 18.78)\n If (feature 0 <= 0.22948000000000002)\n Predict: 16.555555555555557\n Else (feature 0 > 0.22948000000000002)\n Predict: 13.666666666666666\n Else (feature 5 > 6.941)\n If (feature 7 <= 2.0493)\n If (feature 5 <= 7.3454999999999995)\n Predict: 36.0\n Else (feature 5 > 7.3454999999999995)\n Predict: 50.0\n Else (feature 7 > 2.0493)\n If (feature 5 <= 7.414)\n If (feature 10 <= 19.4)\n If (feature 12 <= 5.4399999999999995)\n Predict: 34.48823529411765\n Else (feature 12 > 5.4399999999999995)\n Predict: 30.69000000000001\n Else (feature 10 > 19.4)\n If (feature 0 <= 0.061015)\n Predict: 23.9\n Else (feature 0 > 0.061015)\n Predict: 17.799999999999997\n Else (feature 5 > 7.414)\n If (feature 2 <= 6.305)\n If (feature 11 <= 379.325)\n Predict: 47.23333333333333\n Else (feature 11 > 379.325)\n Predict: 44.7888888888889\n Else (feature 2 > 6.305)\n Predict: 35.200000000000045\n Tree 1 (weight 1.0):\n If (feature 2 <= 6.66)\n If (feature 12 <= 5.2)\n If (feature 5 <= 7.4025)\n If (feature 10 <= 18.35)\n If (feature 9 <= 253.0)\n Predict: 34.02\n Else (feature 9 > 253.0)\n Predict: 31.25\n Else (feature 10 > 18.35)\n If (feature 6 <= 7.3)\n Predict: 29.600000000000005\n Else (feature 6 > 7.3)\n Predict: 26.866666666666664\n Else (feature 5 > 7.4025)\n If (feature 10 <= 14.149999999999999)\n Predict: 50.0\n Else (feature 10 > 14.149999999999999)\n If (feature 9 <= 195.5)\n Predict: 50.0\n Else (feature 9 > 195.5)\n Predict: 44.46428571428572\n Else (feature 12 > 5.2)\n If (feature 10 <= 15.55)\n If (feature 5 <= 7.254)\n If (feature 11 <= 393.79499999999996)\n Predict: 30.366666666666667\n Else (feature 11 > 393.79499999999996)\n Predict: 33.042857142857144\n Else (feature 5 > 7.254)\n If (feature 7 <= 2.1509)\n Predict: 43.1\n Else (feature 7 > 2.1509)\n Predict: 49.20000000000001\n Else (feature 10 > 15.55)\n If (feature 2 <= 2.91)\n If (feature 6 <= 44.9)\n Predict: 20.549999999999997\n Else (feature 6 > 44.9)\n Predict: 27.884210526315787\n Else (feature 2 > 2.91)\n If (feature 9 <= 284.5)\n Predict: 21.645714285714288\n Else (feature 9 > 284.5)\n Predict: 24.170967741935485\n Else (feature 2 > 6.66)\n If (feature 5 <= 6.9715)\n If (feature 0 <= 5.711455)\n If (feature 12 <= 3.825)\n Predict: 50.0\n Else (feature 12 > 3.825)\n If (feature 5 <= 5.8580000000000005)\n Predict: 16.770270270270274\n Else (feature 5 > 5.8580000000000005)\n Predict: 21.08130841121496\n Else (feature 0 > 5.711455)\n If (feature 7 <= 1.8468)\n If (feature 4 <= 0.675)\n Predict: 13.3625\n Else (feature 4 > 0.675)\n Predict: 8.720833333333333\n Else (feature 7 > 1.8468)\n If (feature 7 <= 2.3497000000000003)\n Predict: 14.353846153846156\n Else (feature 7 > 2.3497000000000003)\n Predict: 17.850000000000005\n Else (feature 5 > 6.9715)\n If (feature 5 <= 8.3865)\n Predict: 50.0\n Else (feature 5 > 8.3865)\n Predict: 21.900000000000006\n Tree 2 (weight 1.0):\n If (feature 12 <= 9.95)\n If (feature 7 <= 1.35735)\n Predict: 50.0\n Else (feature 7 > 1.35735)\n If (feature 10 <= 19.4)\n If (feature 12 <= 6.97)\n If (feature 7 <= 2.292)\n Predict: 45.349999999999994\n Else (feature 7 > 2.292)\n Predict: 33.25466666666667\n Else (feature 12 > 6.97)\n If (feature 0 <= 0.53556)\n Predict: 23.93888888888889\n Else (feature 0 > 0.53556)\n Predict: 32.48571428571428\n Else (feature 10 > 19.4)\n If (feature 9 <= 274.5)\n If (feature 8 <= 1.5)\n Predict: 11.9\n Else (feature 8 > 1.5)\n Predict: 20.45\n Else (feature 9 > 274.5)\n If (feature 5 <= 5.6665)\n Predict: 18.2\n Else (feature 5 > 5.6665)\n Predict: 23.67777777777778\n Else (feature 12 > 9.95)\n If (feature 12 <= 16.205)\n If (feature 10 <= 13.3)\n If (feature 6 <= 62.9)\n Predict: 22.8\n Else (feature 6 > 62.9)\n If (feature 0 <= 0.788055)\n Predict: 30.7\n Else (feature 0 > 0.788055)\n Predict: 31.0\n Else (feature 10 > 13.3)\n If (feature 11 <= 92.68)\n If (feature 6 <= 78.80000000000001)\n Predict: 10.55\n Else (feature 6 > 78.80000000000001)\n Predict: 14.366666666666667\n Else (feature 11 > 92.68)\n If (feature 12 <= 14.399999999999999)\n Predict: 21.123943661971836\n Else (feature 12 > 14.399999999999999)\n Predict: 18.89199999999999\n Else (feature 12 > 16.205)\n If (feature 4 <= 0.601)\n If (feature 10 <= 19.4)\n If (feature 12 <= 19.34)\n Predict: 23.299999999999997\n Else (feature 12 > 19.34)\n Predict: 17.25\n Else (feature 10 > 19.4)\n If (feature 4 <= 0.522)\n Predict: 19.5\n Else (feature 4 > 0.522)\n Predict: 14.390909090909092\n Else (feature 4 > 0.601)\n If (feature 0 <= 13.440999999999999)\n If (feature 2 <= 23.77)\n Predict: 14.112\n Else (feature 2 > 23.77)\n Predict: 8.987499999999997\n Else (feature 0 > 13.440999999999999)\n If (feature 5 <= 4.253)\n Predict: 12.533333333333333\n Else (feature 5 > 4.253)\n Predict: 8.883333333333333\n Tree 3 (weight 1.0):\n If (feature 5 <= 6.9715)\n If (feature 9 <= 434.5)\n If (feature 5 <= 6.4925)\n If (feature 7 <= 1.6141)\n If (feature 0 <= 2.2713799999999997)\n Predict: 15.45\n Else (feature 0 > 2.2713799999999997)\n Predict: 14.600000000000001\n Else (feature 7 > 1.6141)\n If (feature 12 <= 7.665)\n Predict: 23.619999999999997\n Else (feature 12 > 7.665)\n Predict: 20.6957264957265\n Else (feature 5 > 6.4925)\n If (feature 12 <= 5.6899999999999995)\n If (feature 11 <= 377.70500000000004)\n Predict: 39.86666666666667\n Else (feature 11 > 377.70500000000004)\n Predict: 30.872727272727275\n Else (feature 12 > 5.6899999999999995)\n If (feature 10 <= 20.95)\n Predict: 25.95\n Else (feature 10 > 20.95)\n Predict: 21.333333333333332\n Else (feature 9 > 434.5)\n If (feature 7 <= 2.0754)\n If (feature 4 <= 0.6695)\n If (feature 12 <= 15.47)\n Predict: 20.472727272727273\n Else (feature 12 > 15.47)\n Predict: 13.071428571428575\n Else (feature 4 > 0.6695)\n If (feature 7 <= 1.5891)\n Predict: 9.241666666666665\n Else (feature 7 > 1.5891)\n Predict: 11.886363636363631\n Else (feature 7 > 2.0754)\n If (feature 11 <= 279.02)\n If (feature 0 <= 3.80591)\n Predict: 19.0\n Else (feature 0 > 3.80591)\n Predict: 13.687500000000004\n Else (feature 11 > 279.02)\n If (feature 12 <= 13.395)\n Predict: 21.017647058823528\n Else (feature 12 > 13.395)\n Predict: 18.9\n Else (feature 5 > 6.9715)\n If (feature 5 <= 7.4715)\n If (feature 12 <= 3.565)\n If (feature 8 <= 5.5)\n If (feature 0 <= 0.016235)\n Predict: 44.0\n Else (feature 0 > 0.016235)\n Predict: 35.5\n Else (feature 8 > 5.5)\n Predict: 50.0\n Else (feature 12 > 3.565)\n If (feature 2 <= 6.66)\n If (feature 4 <= 0.4885)\n Predict: 33.339999999999996\n Else (feature 4 > 0.4885)\n Predict: 28.871428571428574\n Else (feature 2 > 6.66)\n If (feature 0 <= 5.82258)\n Predict: 24.45\n Else (feature 0 > 5.82258)\n Predict: 17.799999999999997\n Else (feature 5 > 7.4715)\n If (feature 6 <= 89.45)\n If (feature 12 <= 3.145)\n If (feature 7 <= 3.23925)\n Predict: 37.6\n Else (feature 7 > 3.23925)\n Predict: 44.14999999999999\n Else (feature 12 > 3.145)\n If (feature 10 <= 17.5)\n Predict: 47.330000000000005\n Else (feature 10 > 17.5)\n Predict: 41.25\n Else (feature 6 > 89.45)\n If (feature 0 <= 1.47984)\n Predict: 50.0\n Else (feature 0 > 1.47984)\n Predict: 50.0\n Tree 4 (weight 1.0):\n If (feature 2 <= 6.66)\n If (feature 5 <= 7.096)\n If (feature 5 <= 6.543)\n If (feature 9 <= 195.5)\n Predict: 29.6\n Else (feature 9 > 195.5)\n If (feature 6 <= 91.35)\n Predict: 21.209090909090904\n Else (feature 6 > 91.35)\n Predict: 16.0\n Else (feature 5 > 6.543)\n If (feature 12 <= 5.07)\n If (feature 5 <= 6.744)\n Predict: 29.016666666666666\n Else (feature 5 > 6.744)\n Predict: 33.283333333333324\n Else (feature 12 > 5.07)\n If (feature 6 <= 21.25)\n Predict: 32.2\n Else (feature 6 > 21.25)\n Predict: 26.555999999999997\n Else (feature 5 > 7.096)\n If (feature 5 <= 7.414)\n If (feature 9 <= 264.5)\n If (feature 2 <= 3.9850000000000003)\n Predict: 36.1\n Else (feature 2 > 3.9850000000000003)\n Predict: 37.3\n Else (feature 9 > 264.5)\n If (feature 6 <= 38.95)\n Predict: 34.199999999999996\n Else (feature 6 > 38.95)\n Predict: 31.849999999999994\n Else (feature 5 > 7.414)\n If (feature 7 <= 2.4218)\n Predict: 50.0\n Else (feature 7 > 2.4218)\n If (feature 9 <= 195.5)\n Predict: 50.0\n Else (feature 9 > 195.5)\n Predict: 43.44375\n Else (feature 2 > 6.66)\n If (feature 0 <= 5.84803)\n If (feature 5 <= 7.07)\n If (feature 5 <= 6.5315)\n If (feature 12 <= 18.095)\n Predict: 20.187128712871285\n Else (feature 12 > 18.095)\n Predict: 15.789655172413813\n Else (feature 5 > 6.5315)\n If (feature 7 <= 1.35735)\n Predict: 50.0\n Else (feature 7 > 1.35735)\n Predict: 24.588235294117652\n Else (feature 5 > 7.07)\n If (feature 0 <= 2.0826200000000004)\n Predict: 50.0\n Else (feature 0 > 2.0826200000000004)\n Predict: 21.899999999999977\n Else (feature 0 > 5.84803)\n If (feature 12 <= 19.0)\n If (feature 12 <= 14.774999999999999)\n If (feature 11 <= 333.245)\n Predict: 27.9\n Else (feature 11 > 333.245)\n Predict: 20.48\n Else (feature 12 > 14.774999999999999)\n If (feature 7 <= 2.3497000000000003)\n Predict: 14.36923076923077\n Else (feature 7 > 2.3497000000000003)\n Predict: 18.450000000000003\n Else (feature 12 > 19.0)\n If (feature 6 <= 90.9)\n If (feature 7 <= 1.8207499999999999)\n Predict: 7.6\n Else (feature 7 > 1.8207499999999999)\n Predict: 11.0\n Else (feature 6 > 90.9)\n If (feature 12 <= 31.4)\n Predict: 11.0\n Else (feature 12 > 31.4)\n Predict: 13.800000000000011\n Tree 5 (weight 1.0):\n If (feature 4 <= 0.6635)\n If (feature 2 <= 3.9850000000000003)\n If (feature 7 <= 6.4889)\n If (feature 5 <= 7.3454999999999995)\n If (feature 9 <= 400.0)\n Predict: 30.341666666666665\n Else (feature 9 > 400.0)\n Predict: 17.899999999999977\n Else (feature 5 > 7.3454999999999995)\n If (feature 7 <= 3.23925)\n Predict: 50.0\n Else (feature 7 > 3.23925)\n Predict: 44.60000000000001\n Else (feature 7 > 6.4889)\n If (feature 6 <= 15.5)\n If (feature 10 <= 14.850000000000001)\n Predict: 29.0\n Else (feature 10 > 14.850000000000001)\n Predict: 34.9\n Else (feature 6 > 15.5)\n If (feature 5 <= 6.6385000000000005)\n Predict: 22.15\n Else (feature 5 > 6.6385000000000005)\n Predict: 30.19999999999999\n Else (feature 2 > 3.9850000000000003)\n If (feature 12 <= 3.825)\n If (feature 2 <= 6.66)\n If (feature 5 <= 6.857)\n Predict: 28.4\n Else (feature 5 > 6.857)\n Predict: 38.96666666666667\n Else (feature 2 > 6.66)\n Predict: 50.0\n Else (feature 12 > 3.825)\n If (feature 5 <= 6.5024999999999995)\n If (feature 12 <= 14.399999999999999)\n Predict: 21.834579439252334\n Else (feature 12 > 14.399999999999999)\n Predict: 18.017241379310338\n Else (feature 5 > 6.5024999999999995)\n If (feature 5 <= 6.941)\n Predict: 24.930555555555554\n Else (feature 5 > 6.941)\n Predict: 34.08333333333335\n Else (feature 4 > 0.6635)\n If (feature 4 <= 0.7064999999999999)\n If (feature 12 <= 25.6)\n If (feature 0 <= 15.43185)\n If (feature 6 <= 93.35)\n Predict: 12.9\n Else (feature 6 > 93.35)\n Predict: 11.0\n Else (feature 0 > 15.43185)\n If (feature 0 <= 24.9239)\n Predict: 8.680000000000001\n Else (feature 0 > 24.9239)\n Predict: 10.9\n Else (feature 12 > 25.6)\n If (feature 5 <= 4.916)\n If (feature 0 <= 11.13425)\n Predict: 13.8\n Else (feature 0 > 11.13425)\n Predict: 10.5\n Else (feature 5 > 4.916)\n If (feature 0 <= 24.021)\n Predict: 7.18\n Else (feature 0 > 24.021)\n Predict: 5.600000000000001\n Else (feature 4 > 0.7064999999999999)\n If (feature 11 <= 305.29499999999996)\n If (feature 7 <= 1.5220500000000001)\n If (feature 11 <= 171.09)\n Predict: 15.6\n Else (feature 11 > 171.09)\n Predict: 13.8\n Else (feature 7 > 1.5220500000000001)\n If (feature 0 <= 8.224335)\n Predict: 13.3\n Else (feature 0 > 8.224335)\n Predict: 8.399999999999999\n Else (feature 11 > 305.29499999999996)\n If (feature 7 <= 1.5293999999999999)\n If (feature 3 <= 0.5)\n Predict: 15.299999999999999\n Else (feature 3 > 0.5)\n Predict: 13.400000000000002\n Else (feature 7 > 1.5293999999999999)\n If (feature 7 <= 2.4579)\n Predict: 18.075\n Else (feature 7 > 2.4579)\n Predict: 21.012499999999996\n Tree 6 (weight 1.0):\n If (feature 12 <= 7.865)\n If (feature 5 <= 7.4715)\n If (feature 7 <= 1.35735)\n Predict: 50.0\n Else (feature 7 > 1.35735)\n If (feature 9 <= 264.5)\n If (feature 2 <= 3.9850000000000003)\n Predict: 34.270833333333336\n Else (feature 2 > 3.9850000000000003)\n Predict: 28.380000000000017\n Else (feature 9 > 264.5)\n If (feature 5 <= 6.7055)\n Predict: 23.66\n Else (feature 5 > 6.7055)\n Predict: 30.36363636363637\n Else (feature 5 > 7.4715)\n If (feature 10 <= 17.5)\n If (feature 10 <= 14.75)\n If (feature 5 <= 7.6274999999999995)\n Predict: 43.1\n Else (feature 5 > 7.6274999999999995)\n Predict: 49.7\n Else (feature 10 > 14.75)\n If (feature 6 <= 70.5)\n Predict: 47.116666666666674\n Else (feature 6 > 70.5)\n Predict: 43.76666666666665\n Else (feature 10 > 17.5)\n If (feature 7 <= 3.51385)\n Predict: 38.7\n Else (feature 7 > 3.51385)\n Predict: 42.8\n Else (feature 12 > 7.865)\n If (feature 4 <= 0.6635)\n If (feature 12 <= 14.805)\n If (feature 5 <= 6.7165)\n If (feature 9 <= 222.5)\n Predict: 25.355555555555558\n Else (feature 9 > 222.5)\n Predict: 21.25257731958763\n Else (feature 5 > 6.7165)\n If (feature 1 <= 6.25)\n Predict: 26.96666666666667\n Else (feature 1 > 6.25)\n Predict: 31.9\n Else (feature 12 > 14.805)\n If (feature 0 <= 0.220825)\n If (feature 11 <= 386.68)\n Predict: 17.85\n Else (feature 11 > 386.68)\n Predict: 23.388888888888886\n Else (feature 0 > 0.220825)\n If (feature 0 <= 5.711455)\n Predict: 16.91304347826087\n Else (feature 0 > 5.711455)\n Predict: 13.487499999999997\n Else (feature 4 > 0.6635)\n If (feature 12 <= 14.315)\n If (feature 9 <= 407.0)\n Predict: 19.6\n Else (feature 9 > 407.0)\n If (feature 12 <= 14.045)\n Predict: 21.7\n Else (feature 12 > 14.045)\n Predict: 19.900000000000006\n Else (feature 12 > 14.315)\n If (feature 0 <= 10.014420000000001)\n If (feature 4 <= 0.7064999999999999)\n Predict: 11.414285714285713\n Else (feature 4 > 0.7064999999999999)\n Predict: 14.482608695652175\n Else (feature 0 > 10.014420000000001)\n If (feature 7 <= 1.3513000000000002)\n Predict: 13.850000000000001\n Else (feature 7 > 1.3513000000000002)\n Predict: 8.847058823529414\n Tree 7 (weight 1.0):\n If (feature 8 <= 16.0)\n If (feature 5 <= 6.797)\n If (feature 12 <= 7.76)\n If (feature 5 <= 6.543)\n If (feature 12 <= 5.57)\n Predict: 26.24\n Else (feature 12 > 5.57)\n Predict: 23.651999999999997\n Else (feature 5 > 6.543)\n If (feature 6 <= 81.05)\n Predict: 27.983333333333334\n Else (feature 6 > 81.05)\n Predict: 22.400000000000006\n Else (feature 12 > 7.76)\n If (feature 5 <= 6.0985)\n If (feature 0 <= 0.7673049999999999)\n Predict: 19.454545454545457\n Else (feature 0 > 0.7673049999999999)\n Predict: 14.999999999999988\n Else (feature 5 > 6.0985)\n If (feature 5 <= 6.611)\n Predict: 21.006249999999998\n Else (feature 5 > 6.611)\n Predict: 26.31428571428572\n Else (feature 5 > 6.797)\n If (feature 4 <= 0.5125)\n If (feature 5 <= 7.414)\n If (feature 7 <= 7.3131)\n Predict: 33.227272727272734\n Else (feature 7 > 7.3131)\n Predict: 28.239999999999963\n Else (feature 5 > 7.414)\n If (feature 6 <= 50.35)\n Predict: 44.01666666666667\n Else (feature 6 > 50.35)\n Predict: 36.666666666666664\n Else (feature 4 > 0.5125)\n If (feature 12 <= 7.475)\n If (feature 7 <= 1.88595)\n Predict: 43.474999999999994\n Else (feature 7 > 1.88595)\n Predict: 48.75384615384615\n Else (feature 12 > 7.475)\n Predict: 30.69999999999999\n Else (feature 8 > 16.0)\n If (feature 7 <= 2.2085)\n If (feature 0 <= 13.95715)\n If (feature 5 <= 4.9715)\n Predict: 22.7\n Else (feature 5 > 4.9715)\n If (feature 11 <= 305.29499999999996)\n Predict: 10.952941176470588\n Else (feature 11 > 305.29499999999996)\n Predict: 16.228571428571435\n Else (feature 0 > 13.95715)\n If (feature 11 <= 363.225)\n If (feature 7 <= 1.3513000000000002)\n Predict: 13.9\n Else (feature 7 > 1.3513000000000002)\n Predict: 10.483333333333333\n Else (feature 11 > 363.225)\n If (feature 12 <= 22.424999999999997)\n Predict: 9.333333333333334\n Else (feature 12 > 22.424999999999997)\n Predict: 6.533333333333336\n Else (feature 7 > 2.2085)\n If (feature 11 <= 279.02)\n Predict: 14.9\n Else (feature 11 > 279.02)\n If (feature 4 <= 0.5805)\n If (feature 6 <= 57.3)\n Predict: 20.950000000000003\n Else (feature 6 > 57.3)\n Predict: 23.65\n Else (feature 4 > 0.5805)\n If (feature 12 <= 13.245000000000001)\n Predict: 22.25\n Else (feature 12 > 13.245000000000001)\n Predict: 18.846153846153843\n Tree 8 (weight 1.0):\n If (feature 5 <= 6.824999999999999)\n If (feature 12 <= 14.399999999999999)\n If (feature 5 <= 6.543)\n If (feature 12 <= 7.82)\n If (feature 9 <= 222.5)\n Predict: 28.7\n Else (feature 9 > 222.5)\n Predict: 23.823529411764707\n Else (feature 12 > 7.82)\n If (feature 10 <= 20.95)\n Predict: 21.325984251968503\n Else (feature 10 > 20.95)\n Predict: 17.25\n Else (feature 5 > 6.543)\n If (feature 2 <= 3.54)\n If (feature 12 <= 5.6899999999999995)\n Predict: 31.025\n Else (feature 12 > 5.6899999999999995)\n Predict: 28.400000000000006\n Else (feature 2 > 3.54)\n If (feature 11 <= 395.76)\n Predict: 24.5\n Else (feature 11 > 395.76)\n Predict: 27.46\n Else (feature 12 > 14.399999999999999)\n If (feature 9 <= 453.0)\n If (feature 12 <= 18.095)\n If (feature 11 <= 311.205)\n Predict: 15.066666666666666\n Else (feature 11 > 311.205)\n Predict: 19.009090909090904\n Else (feature 12 > 18.095)\n If (feature 6 <= 9.850000000000001)\n Predict: 23.7\n Else (feature 6 > 9.850000000000001)\n Predict: 14.378571428571432\n Else (feature 9 > 453.0)\n If (feature 0 <= 5.711455)\n If (feature 6 <= 85.15)\n Predict: 21.125\n Else (feature 6 > 85.15)\n Predict: 15.85\n Else (feature 0 > 5.711455)\n If (feature 12 <= 25.6)\n Predict: 12.186842105263159\n Else (feature 12 > 25.6)\n Predict: 8.186363636363637\n Else (feature 5 > 6.824999999999999)\n If (feature 12 <= 7.865)\n If (feature 7 <= 1.97915)\n If (feature 6 <= 97.65)\n If (feature 5 <= 6.946999999999999)\n Predict: 41.3\n Else (feature 5 > 6.946999999999999)\n Predict: 50.0\n Else (feature 6 > 97.65)\n Predict: 36.00000000000003\n Else (feature 7 > 1.97915)\n If (feature 5 <= 7.414)\n If (feature 12 <= 5.495)\n Predict: 33.58620689655172\n Else (feature 12 > 5.495)\n Predict: 28.963636363636365\n Else (feature 5 > 7.414)\n If (feature 8 <= 3.5)\n Predict: 39.55555555555556\n Else (feature 8 > 3.5)\n Predict: 45.23333333333335\n Else (feature 12 > 7.865)\n If (feature 6 <= 84.65)\n If (feature 7 <= 3.2759)\n If (feature 11 <= 384.185)\n Predict: 30.7\n Else (feature 11 > 384.185)\n Predict: 27.500000000000004\n Else (feature 7 > 3.2759)\n Predict: 22.0\n Else (feature 6 > 84.65)\n If (feature 7 <= 1.4178000000000002)\n Predict: 10.4\n Else (feature 7 > 1.4178000000000002)\n Predict: 17.8\n Tree 9 (weight 1.0):\n If (feature 5 <= 6.9715)\n If (feature 10 <= 19.9)\n If (feature 12 <= 7.475)\n If (feature 12 <= 4.640000000000001)\n If (feature 1 <= 23.5)\n Predict: 26.400000000000002\n Else (feature 1 > 23.5)\n Predict: 32.5\n Else (feature 12 > 4.640000000000001)\n If (feature 5 <= 6.519)\n Predict: 23.685714285714287\n Else (feature 5 > 6.519)\n Predict: 27.973076923076935\n Else (feature 12 > 7.475)\n If (feature 4 <= 0.651)\n If (feature 5 <= 6.0954999999999995)\n Predict: 20.595454545454544\n Else (feature 5 > 6.0954999999999995)\n Predict: 23.066129032258065\n Else (feature 4 > 0.651)\n If (feature 5 <= 5.634)\n Predict: 14.454545454545451\n Else (feature 5 > 5.634)\n Predict: 21.5\n Else (feature 10 > 19.9)\n If (feature 7 <= 1.1333)\n Predict: 50.0\n Else (feature 7 > 1.1333)\n If (feature 4 <= 0.6635)\n If (feature 7 <= 1.3387)\n Predict: 26.485714285714288\n Else (feature 7 > 1.3387)\n Predict: 18.009677419354837\n Else (feature 4 > 0.6635)\n If (feature 7 <= 2.0754)\n Predict: 11.633333333333335\n Else (feature 7 > 2.0754)\n Predict: 18.914285714285718\n Else (feature 5 > 6.9715)\n If (feature 2 <= 18.84)\n If (feature 12 <= 4.475)\n If (feature 1 <= 23.5)\n If (feature 5 <= 7.926)\n Predict: 46.9\n Else (feature 5 > 7.926)\n Predict: 40.74444444444445\n Else (feature 1 > 23.5)\n If (feature 12 <= 3.565)\n Predict: 37.0\n Else (feature 12 > 3.565)\n Predict: 33.099999999999994\n Else (feature 12 > 4.475)\n If (feature 2 <= 7.015000000000001)\n If (feature 0 <= 0.056975)\n Predict: 30.057142857142853\n Else (feature 0 > 0.056975)\n Predict: 36.023529411764706\n Else (feature 2 > 7.015000000000001)\n If (feature 0 <= 5.82258)\n Predict: 23.174999999999997\n Else (feature 0 > 5.82258)\n Predict: 17.799999999999997\n Else (feature 2 > 18.84)\n Predict: 50.0\n Tree 10 (weight 1.0):\n If (feature 4 <= 0.6695)\n If (feature 12 <= 5.945)\n If (feature 0 <= 0.127795)\n If (feature 5 <= 7.33)\n If (feature 12 <= 5.109999999999999)\n Predict: 33.076190476190476\n Else (feature 12 > 5.109999999999999)\n Predict: 27.57500000000001\n Else (feature 5 > 7.33)\n If (feature 6 <= 50.35)\n Predict: 44.379999999999995\n Else (feature 6 > 50.35)\n Predict: 38.69999999999999\n Else (feature 0 > 0.127795)\n If (feature 6 <= 73.35)\n If (feature 12 <= 4.015000000000001)\n Predict: 45.56666666666666\n Else (feature 12 > 4.015000000000001)\n Predict: 31.5\n Else (feature 6 > 73.35)\n If (feature 12 <= 3.29)\n Predict: 50.0\n Else (feature 12 > 3.29)\n Predict: 45.54999999999999\n Else (feature 12 > 5.945)\n If (feature 9 <= 332.0)\n If (feature 5 <= 6.954)\n If (feature 12 <= 9.775)\n Predict: 23.9\n Else (feature 12 > 9.775)\n Predict: 20.85961538461538\n Else (feature 5 > 6.954)\n If (feature 12 <= 8.129999999999999)\n Predict: 37.14545454545455\n Else (feature 12 > 8.129999999999999)\n Predict: 31.220000000000006\n Else (feature 9 > 332.0)\n If (feature 3 <= 0.5)\n If (feature 12 <= 15.0)\n Predict: 21.368852459016395\n Else (feature 12 > 15.0)\n Predict: 15.837837837837844\n Else (feature 3 > 0.5)\n Predict: 50.0\n Else (feature 4 > 0.6695)\n If (feature 12 <= 15.024999999999999)\n If (feature 0 <= 7.08414)\n If (feature 11 <= 353.46500000000003)\n If (feature 12 <= 14.315)\n Predict: 20.9\n Else (feature 12 > 14.315)\n Predict: 16.800000000000004\n Else (feature 11 > 353.46500000000003)\n If (feature 11 <= 390.755)\n Predict: 23.080000000000002\n Else (feature 11 > 390.755)\n Predict: 20.799999999999997\n Else (feature 0 > 7.08414)\n Predict: 16.69999999999999\n Else (feature 12 > 15.024999999999999)\n If (feature 12 <= 18.78)\n If (feature 12 <= 18.235)\n If (feature 5 <= 6.776)\n Predict: 14.000000000000002\n Else (feature 5 > 6.776)\n Predict: 17.799999999999997\n Else (feature 12 > 18.235)\n Predict: 23.19999999999999\n Else (feature 12 > 18.78)\n If (feature 0 <= 8.17616)\n If (feature 7 <= 1.5293999999999999)\n Predict: 14.100000000000001\n Else (feature 7 > 1.5293999999999999)\n Predict: 11.825\n Else (feature 0 > 8.17616)\n If (feature 7 <= 1.3989500000000001)\n Predict: 12.88\n Else (feature 7 > 1.3989500000000001)\n Predict: 8.715\n Tree 11 (weight 1.0):\n If (feature 12 <= 7.865)\n If (feature 12 <= 4.015000000000001)\n If (feature 5 <= 7.0105)\n If (feature 11 <= 375.42499999999995)\n Predict: 50.0\n Else (feature 11 > 375.42499999999995)\n If (feature 5 <= 6.4399999999999995)\n Predict: 24.8\n Else (feature 5 > 6.4399999999999995)\n Predict: 31.666666666666668\n Else (feature 5 > 7.0105)\n If (feature 3 <= 0.5)\n If (feature 12 <= 3.55)\n Predict: 41.1\n Else (feature 12 > 3.55)\n Predict: 48.29999999999998\n Else (feature 3 > 0.5)\n Predict: 50.0\n Else (feature 12 > 4.015000000000001)\n If (feature 5 <= 6.797)\n If (feature 10 <= 16.05)\n If (feature 2 <= 3.54)\n Predict: 30.766666666666666\n Else (feature 2 > 3.54)\n Predict: 27.800000000000004\n Else (feature 10 > 16.05)\n If (feature 2 <= 4.895)\n Predict: 25.61111111111111\n Else (feature 2 > 4.895)\n Predict: 23.32222222222222\n Else (feature 5 > 6.797)\n If (feature 10 <= 14.75)\n If (feature 7 <= 2.292)\n Predict: 43.68333333333334\n Else (feature 7 > 2.292)\n Predict: 33.95\n Else (feature 10 > 14.75)\n If (feature 10 <= 18.75)\n Predict: 33.400000000000006\n Else (feature 10 > 18.75)\n Predict: 23.966666666666697\n Else (feature 12 > 7.865)\n If (feature 7 <= 1.9198499999999998)\n If (feature 7 <= 1.1333)\n Predict: 50.0\n Else (feature 7 > 1.1333)\n If (feature 4 <= 0.6695)\n If (feature 12 <= 20.195)\n Predict: 17.822222222222223\n Else (feature 12 > 20.195)\n Predict: 11.600000000000003\n Else (feature 4 > 0.6695)\n If (feature 0 <= 4.159895000000001)\n Predict: 16.7\n Else (feature 0 > 4.159895000000001)\n Predict: 10.340909090909088\n Else (feature 7 > 1.9198499999999998)\n If (feature 0 <= 5.84803)\n If (feature 10 <= 13.3)\n If (feature 0 <= 0.553925)\n Predict: 35.15\n Else (feature 0 > 0.553925)\n Predict: 30.85\n Else (feature 10 > 13.3)\n If (feature 12 <= 14.399999999999999)\n Predict: 22.169892473118292\n Else (feature 12 > 14.399999999999999)\n Predict: 18.91470588235295\n Else (feature 0 > 5.84803)\n If (feature 7 <= 2.8001)\n If (feature 11 <= 379.86)\n Predict: 14.4\n Else (feature 11 > 379.86)\n Predict: 18.933333333333326\n Else (feature 7 > 2.8001)\n If (feature 0 <= 8.07211)\n Predict: 21.4\n Else (feature 0 > 8.07211)\n Predict: 19.1\n Tree 12 (weight 1.0):\n If (feature 8 <= 16.0)\n If (feature 2 <= 6.66)\n If (feature 5 <= 6.9625)\n If (feature 12 <= 7.665)\n If (feature 5 <= 6.4925)\n Predict: 23.08888888888889\n Else (feature 5 > 6.4925)\n Predict: 28.670370370370375\n Else (feature 12 > 7.665)\n If (feature 2 <= 6.0649999999999995)\n Predict: 20.0375\n Else (feature 2 > 6.0649999999999995)\n Predict: 24.911111111111115\n Else (feature 5 > 6.9625)\n If (feature 5 <= 7.418)\n If (feature 4 <= 0.4885)\n Predict: 34.344827586206904\n Else (feature 4 > 0.4885)\n Predict: 29.37500000000003\n Else (feature 5 > 7.418)\n If (feature 5 <= 8.281500000000001)\n Predict: 42.10555555555556\n Else (feature 5 > 8.281500000000001)\n Predict: 49.39999999999998\n Else (feature 2 > 6.66)\n If (feature 5 <= 6.797)\n If (feature 5 <= 5.8580000000000005)\n If (feature 6 <= 73.19999999999999)\n Predict: 20.2\n Else (feature 6 > 73.19999999999999)\n Predict: 15.578260869565218\n Else (feature 5 > 5.8580000000000005)\n If (feature 6 <= 58.75)\n Predict: 23.847619047619045\n Else (feature 6 > 58.75)\n Predict: 20.73846153846153\n Else (feature 5 > 6.797)\n Predict: 50.0\n Else (feature 8 > 16.0)\n If (feature 12 <= 8.905000000000001)\n If (feature 0 <= 4.647145)\n If (feature 11 <= 354.92)\n Predict: 21.9\n Else (feature 11 > 354.92)\n Predict: 25.0\n Else (feature 0 > 4.647145)\n Predict: 50.0\n Else (feature 12 > 8.905000000000001)\n If (feature 0 <= 15.718)\n If (feature 12 <= 15.0)\n If (feature 7 <= 1.3034)\n Predict: 27.9\n Else (feature 7 > 1.3034)\n Predict: 20.215\n Else (feature 12 > 15.0)\n If (feature 11 <= 338.395)\n Predict: 11.969565217391304\n Else (feature 11 > 338.395)\n Predict: 14.361538461538462\n Else (feature 0 > 15.718)\n If (feature 4 <= 0.6695)\n If (feature 7 <= 1.1761499999999998)\n Predict: 13.800000000000002\n Else (feature 7 > 1.1761499999999998)\n Predict: 15.0\n Else (feature 4 > 0.6695)\n If (feature 7 <= 1.4838)\n Predict: 9.542857142857143\n Else (feature 7 > 1.4838)\n Predict: 7.7500000000000036\n Tree 13 (weight 1.0):\n If (feature 12 <= 5.495)\n If (feature 7 <= 2.8954500000000003)\n If (feature 6 <= 82.95)\n If (feature 7 <= 1.9071500000000001)\n Predict: 21.9\n Else (feature 7 > 1.9071500000000001)\n If (feature 0 <= 0.057844999999999994)\n Predict: 37.2\n Else (feature 0 > 0.057844999999999994)\n Predict: 44.8\n Else (feature 6 > 82.95)\n Predict: 50.0\n Else (feature 7 > 2.8954500000000003)\n If (feature 7 <= 3.3388999999999998)\n Predict: 22.8\n Else (feature 7 > 3.3388999999999998)\n If (feature 12 <= 3.58)\n If (feature 9 <= 222.5)\n Predict: 33.4\n Else (feature 9 > 222.5)\n Predict: 40.08181818181818\n Else (feature 12 > 3.58)\n If (feature 6 <= 9.350000000000001)\n Predict: 24.433333333333334\n Else (feature 6 > 9.350000000000001)\n Predict: 31.830000000000005\n Else (feature 12 > 5.495)\n If (feature 2 <= 16.57)\n If (feature 4 <= 0.574)\n If (feature 12 <= 9.95)\n If (feature 9 <= 222.5)\n Predict: 33.78333333333333\n Else (feature 9 > 222.5)\n Predict: 23.789130434782606\n Else (feature 12 > 9.95)\n If (feature 5 <= 5.9670000000000005)\n Predict: 18.144736842105264\n Else (feature 5 > 5.9670000000000005)\n Predict: 21.553846153846152\n Else (feature 4 > 0.574)\n If (feature 12 <= 14.805)\n If (feature 7 <= 2.1509)\n Predict: 30.6\n Else (feature 7 > 2.1509)\n Predict: 50.0\n Else (feature 12 > 14.805)\n Predict: 17.5\n Else (feature 2 > 16.57)\n If (feature 7 <= 1.1333)\n Predict: 50.0\n Else (feature 7 > 1.1333)\n If (feature 7 <= 2.0754)\n If (feature 8 <= 5.5)\n Predict: 16.148275862068967\n Else (feature 8 > 5.5)\n Predict: 11.871698113207543\n Else (feature 7 > 2.0754)\n If (feature 12 <= 14.399999999999999)\n Predict: 21.938095238095237\n Else (feature 12 > 14.399999999999999)\n Predict: 16.912\n Tree 14 (weight 1.0):\n If (feature 5 <= 6.9715)\n If (feature 12 <= 14.774999999999999)\n If (feature 3 <= 0.5)\n If (feature 4 <= 0.5125)\n If (feature 12 <= 6.925)\n Predict: 27.53448275862069\n Else (feature 12 > 6.925)\n Predict: 22.163265306122454\n Else (feature 4 > 0.5125)\n If (feature 0 <= 0.047799999999999995)\n Predict: 13.64\n Else (feature 0 > 0.047799999999999995)\n Predict: 21.54137931034483\n Else (feature 3 > 0.5)\n If (feature 7 <= 1.1333)\n Predict: 50.0\n Else (feature 7 > 1.1333)\n If (feature 12 <= 9.95)\n Predict: 27.4\n Else (feature 12 > 9.95)\n Predict: 22.200000000000003\n Else (feature 12 > 14.774999999999999)\n If (feature 7 <= 2.0754)\n If (feature 0 <= 8.887540000000001)\n If (feature 8 <= 4.5)\n Predict: 16.837500000000002\n Else (feature 8 > 4.5)\n Predict: 13.096153846153847\n Else (feature 0 > 8.887540000000001)\n If (feature 12 <= 22.424999999999997)\n Predict: 11.716666666666667\n Else (feature 12 > 22.424999999999997)\n Predict: 8.58214285714286\n Else (feature 7 > 2.0754)\n If (feature 6 <= 79.85)\n If (feature 12 <= 16.205)\n Predict: 19.663636363636364\n Else (feature 12 > 16.205)\n Predict: 22.357142857142858\n Else (feature 6 > 79.85)\n If (feature 11 <= 311.205)\n Predict: 13.7\n Else (feature 11 > 311.205)\n Predict: 18.168421052631576\n Else (feature 5 > 6.9715)\n If (feature 12 <= 4.475)\n If (feature 3 <= 0.5)\n If (feature 7 <= 3.3765)\n If (feature 5 <= 7.9845)\n Predict: 49.339999999999996\n Else (feature 5 > 7.9845)\n Predict: 40.0\n Else (feature 7 > 3.3765)\n If (feature 4 <= 0.4045)\n Predict: 33.3\n Else (feature 4 > 0.4045)\n Predict: 39.4625\n Else (feature 3 > 0.5)\n Predict: 50.0\n Else (feature 12 > 4.475)\n If (feature 11 <= 389.705)\n If (feature 1 <= 6.25)\n Predict: 31.7\n Else (feature 1 > 6.25)\n If (feature 5 <= 7.3454999999999995)\n Predict: 36.0\n Else (feature 5 > 7.3454999999999995)\n Predict: 49.400000000000006\n Else (feature 11 > 389.705)\n If (feature 6 <= 72.2)\n If (feature 9 <= 223.5)\n Predict: 35.45\n Else (feature 9 > 223.5)\n Predict: 32.169999999999995\n Else (feature 6 > 72.2)\n If (feature 12 <= 7.074999999999999)\n Predict: 24.375\n Else (feature 12 > 7.074999999999999)\n Predict: 32.400000000000006\n Tree 15 (weight 1.0):\n If (feature 12 <= 9.95)\n If (feature 7 <= 2.4218)\n If (feature 0 <= 1.365415)\n If (feature 5 <= 7.221)\n If (feature 6 <= 91.69999999999999)\n Predict: 35.6\n Else (feature 6 > 91.69999999999999)\n Predict: 26.10000000000001\n Else (feature 5 > 7.221)\n If (feature 12 <= 7.38)\n Predict: 46.760000000000005\n Else (feature 12 > 7.38)\n Predict: 50.0\n Else (feature 0 > 1.365415)\n Predict: 50.00000000000001\n Else (feature 7 > 2.4218)\n If (feature 12 <= 5.495)\n If (feature 1 <= 20.5)\n If (feature 6 <= 17.25)\n Predict: 46.7\n Else (feature 6 > 17.25)\n Predict: 34.838461538461544\n Else (feature 1 > 20.5)\n If (feature 0 <= 0.07082)\n Predict: 30.877777777777776\n Else (feature 0 > 0.07082)\n Predict: 36.17999999999999\n Else (feature 12 > 5.495)\n If (feature 7 <= 4.445550000000001)\n If (feature 5 <= 6.1215)\n Predict: 19.683333333333334\n Else (feature 5 > 6.1215)\n Predict: 26.956250000000008\n Else (feature 7 > 4.445550000000001)\n If (feature 5 <= 6.6385000000000005)\n Predict: 22.405660377358487\n Else (feature 5 > 6.6385000000000005)\n Predict: 28.23333333333335\n Else (feature 12 > 9.95)\n If (feature 12 <= 19.34)\n If (feature 11 <= 311.205)\n If (feature 4 <= 0.619)\n If (feature 12 <= 14.115)\n Predict: 20.849999999999998\n Else (feature 12 > 14.115)\n Predict: 14.64285714285714\n Else (feature 4 > 0.619)\n If (feature 12 <= 14.58)\n Predict: 10.9\n Else (feature 12 > 14.58)\n Predict: 13.528571428571428\n Else (feature 11 > 311.205)\n If (feature 2 <= 3.9850000000000003)\n If (feature 0 <= 0.7673049999999999)\n Predict: 22.98\n Else (feature 0 > 0.7673049999999999)\n Predict: 30.7\n Else (feature 2 > 3.9850000000000003)\n If (feature 4 <= 0.607)\n Predict: 20.736\n Else (feature 4 > 0.607)\n Predict: 18.902702702702697\n Else (feature 12 > 19.34)\n If (feature 8 <= 5.5)\n If (feature 5 <= 5.1240000000000006)\n If (feature 9 <= 453.0)\n Predict: 14.5\n Else (feature 9 > 453.0)\n Predict: 8.100000000000001\n Else (feature 5 > 5.1240000000000006)\n If (feature 2 <= 20.735)\n Predict: 15.085714285714285\n Else (feature 2 > 20.735)\n Predict: 13.300000000000002\n Else (feature 8 > 5.5)\n If (feature 7 <= 1.3989500000000001)\n If (feature 7 <= 1.3720500000000002)\n Predict: 13.257142857142858\n Else (feature 7 > 1.3720500000000002)\n Predict: 11.649999999999999\n Else (feature 7 > 1.3989500000000001)\n If (feature 0 <= 9.7096)\n Predict: 11.554545454545455\n Else (feature 0 > 9.7096)\n Predict: 7.507142857142858\n Tree 16 (weight 1.0):\n If (feature 5 <= 6.9715)\n If (feature 12 <= 14.364999999999998)\n If (feature 5 <= 6.543)\n If (feature 12 <= 7.76)\n If (feature 2 <= 13.36)\n Predict: 23.665384615384617\n Else (feature 2 > 13.36)\n Predict: 27.0\n Else (feature 12 > 7.76)\n If (feature 8 <= 1.5)\n Predict: 16.125\n Else (feature 8 > 1.5)\n Predict: 21.306542056074765\n Else (feature 5 > 6.543)\n If (feature 2 <= 14.48)\n If (feature 9 <= 298.0)\n Predict: 29.04736842105263\n Else (feature 9 > 298.0)\n Predict: 25.84399999999999\n Else (feature 2 > 14.48)\n Predict: 50.0\n Else (feature 12 > 14.364999999999998)\n If (feature 12 <= 19.0)\n If (feature 10 <= 19.4)\n If (feature 10 <= 18.45)\n Predict: 19.316666666666666\n Else (feature 10 > 18.45)\n Predict: 21.6125\n Else (feature 10 > 19.4)\n If (feature 11 <= 311.205)\n Predict: 13.654545454545454\n Else (feature 11 > 311.205)\n Predict: 16.99615384615384\n Else (feature 12 > 19.0)\n If (feature 9 <= 453.0)\n If (feature 8 <= 5.5)\n Predict: 14.294444444444444\n Else (feature 8 > 5.5)\n Predict: 21.69999999999999\n Else (feature 9 > 453.0)\n If (feature 7 <= 1.96955)\n Predict: 9.762500000000001\n Else (feature 7 > 1.96955)\n Predict: 13.499999999999991\n Else (feature 5 > 6.9715)\n If (feature 5 <= 7.4715)\n If (feature 7 <= 1.3034)\n Predict: 50.0\n Else (feature 7 > 1.3034)\n If (feature 6 <= 84.65)\n If (feature 10 <= 14.850000000000001)\n Predict: 30.28888888888889\n Else (feature 10 > 14.850000000000001)\n Predict: 34.13928571428572\n Else (feature 6 > 84.65)\n If (feature 1 <= 6.25)\n Predict: 20.700000000000003\n Else (feature 1 > 6.25)\n Predict: 31.0\n Else (feature 5 > 7.4715)\n If (feature 10 <= 18.1)\n If (feature 12 <= 3.145)\n If (feature 7 <= 3.23925)\n Predict: 37.6\n Else (feature 7 > 3.23925)\n Predict: 43.333333333333336\n Else (feature 12 > 3.145)\n If (feature 10 <= 17.85)\n Predict: 48.0\n Else (feature 10 > 17.85)\n Predict: 41.25\n Else (feature 10 > 18.1)\n If (feature 2 <= 7.015000000000001)\n Predict: 35.2\n Else (feature 2 > 7.015000000000001)\n Predict: 21.9\n Tree 17 (weight 1.0):\n If (feature 10 <= 19.65)\n If (feature 7 <= 1.7548)\n If (feature 0 <= 1.72844)\n Predict: 21.5\n Else (feature 0 > 1.72844)\n If (feature 5 <= 4.9045)\n Predict: 11.8\n Else (feature 5 > 4.9045)\n If (feature 0 <= 2.378955)\n Predict: 15.1\n Else (feature 0 > 2.378955)\n Predict: 13.36\n Else (feature 7 > 1.7548)\n If (feature 10 <= 15.05)\n If (feature 12 <= 7.38)\n If (feature 7 <= 2.292)\n Predict: 47.2\n Else (feature 7 > 2.292)\n Predict: 33.616666666666674\n Else (feature 12 > 7.38)\n If (feature 7 <= 1.94)\n Predict: 36.5\n Else (feature 7 > 1.94)\n Predict: 26.214285714285715\n Else (feature 10 > 15.05)\n If (feature 12 <= 6.705)\n If (feature 5 <= 7.4715)\n Predict: 30.133333333333336\n Else (feature 5 > 7.4715)\n Predict: 42.25\n Else (feature 12 > 6.705)\n If (feature 5 <= 6.954)\n Predict: 22.28157894736842\n Else (feature 5 > 6.954)\n Predict: 32.799999999999955\n Else (feature 10 > 19.65)\n If (feature 7 <= 1.3387)\n If (feature 7 <= 1.1902499999999998)\n If (feature 7 <= 1.1761499999999998)\n Predict: 13.8\n Else (feature 7 > 1.1761499999999998)\n Predict: 11.900000000000002\n Else (feature 7 > 1.1902499999999998)\n Predict: 50.0\n Else (feature 7 > 1.3387)\n If (feature 6 <= 89.1)\n If (feature 0 <= 6.7594449999999995)\n If (feature 12 <= 14.16)\n Predict: 21.014285714285712\n Else (feature 12 > 14.16)\n Predict: 17.366666666666674\n Else (feature 0 > 6.7594449999999995)\n If (feature 7 <= 1.94)\n Predict: 9.7\n Else (feature 7 > 1.94)\n Predict: 17.359999999999996\n Else (feature 6 > 89.1)\n If (feature 7 <= 2.0941)\n If (feature 12 <= 20.195)\n Predict: 14.43030303030303\n Else (feature 12 > 20.195)\n Predict: 10.009375000000004\n Else (feature 7 > 2.0941)\n If (feature 11 <= 376.65999999999997)\n Predict: 15.455555555555556\n Else (feature 11 > 376.65999999999997)\n Predict: 19.16428571428571\n Tree 18 (weight 1.0):\n If (feature 5 <= 6.797)\n If (feature 12 <= 14.364999999999998)\n If (feature 7 <= 1.1333)\n Predict: 50.0\n Else (feature 7 > 1.1333)\n If (feature 4 <= 0.5125)\n If (feature 5 <= 6.543)\n Predict: 22.88260869565218\n Else (feature 5 > 6.543)\n Predict: 27.48800000000001\n Else (feature 4 > 0.5125)\n If (feature 7 <= 1.4178000000000002)\n Predict: 15.0\n Else (feature 7 > 1.4178000000000002)\n Predict: 21.31764705882353\n Else (feature 12 > 14.364999999999998)\n If (feature 12 <= 19.34)\n If (feature 4 <= 0.5834999999999999)\n If (feature 10 <= 19.15)\n Predict: 21.327272727272728\n Else (feature 10 > 19.15)\n Predict: 18.075000000000003\n Else (feature 4 > 0.5834999999999999)\n If (feature 9 <= 394.5)\n Predict: 20.3\n Else (feature 9 > 394.5)\n Predict: 15.841666666666665\n Else (feature 12 > 19.34)\n If (feature 4 <= 0.675)\n If (feature 0 <= 0.29386999999999996)\n Predict: 23.7\n Else (feature 0 > 0.29386999999999996)\n Predict: 13.388888888888891\n Else (feature 4 > 0.675)\n If (feature 4 <= 0.6964999999999999)\n Predict: 7.133333333333333\n Else (feature 4 > 0.6964999999999999)\n Predict: 10.525\n Else (feature 5 > 6.797)\n If (feature 2 <= 18.84)\n If (feature 5 <= 7.4715)\n If (feature 0 <= 0.83865)\n If (feature 5 <= 6.941)\n Predict: 29.615384615384617\n Else (feature 5 > 6.941)\n Predict: 34.41944444444445\n Else (feature 0 > 0.83865)\n If (feature 6 <= 92.15)\n Predict: 10.4\n Else (feature 6 > 92.15)\n Predict: 17.800000000000004\n Else (feature 5 > 7.4715)\n If (feature 9 <= 332.0)\n If (feature 9 <= 264.5)\n Predict: 47.95454545454545\n Else (feature 9 > 264.5)\n Predict: 40.5625\n Else (feature 9 > 332.0)\n Predict: 21.899999999999977\n Else (feature 2 > 18.84)\n If (feature 0 <= 1.237685)\n Predict: 41.3\n Else (feature 0 > 1.237685)\n If (feature 0 <= 1.62073)\n Predict: 50.0\n Else (feature 0 > 1.62073)\n Predict: 50.0\n Tree 19 (weight 1.0):\n If (feature 4 <= 0.6635)\n If (feature 12 <= 4.475)\n If (feature 1 <= 6.25)\n If (feature 2 <= 6.305)\n If (feature 0 <= 0.339945)\n Predict: 47.04\n Else (feature 0 > 0.339945)\n Predict: 38.96666666666666\n Else (feature 2 > 6.305)\n Predict: 50.0\n Else (feature 1 > 6.25)\n If (feature 5 <= 7.33)\n If (feature 5 <= 6.9625)\n Predict: 30.1\n Else (feature 5 > 6.9625)\n Predict: 34.559999999999995\n Else (feature 5 > 7.33)\n If (feature 0 <= 0.015195)\n Predict: 50.0\n Else (feature 0 > 0.015195)\n Predict: 43.400000000000006\n Else (feature 12 > 4.475)\n If (feature 6 <= 93.15)\n If (feature 12 <= 8.075)\n If (feature 5 <= 6.797)\n Predict: 24.474999999999998\n Else (feature 5 > 6.797)\n Predict: 32.9025641025641\n Else (feature 12 > 8.075)\n If (feature 12 <= 14.399999999999999)\n Predict: 22.343478260869563\n Else (feature 12 > 14.399999999999999)\n Predict: 19.435714285714294\n Else (feature 6 > 93.15)\n If (feature 7 <= 1.7616)\n If (feature 7 <= 1.1902499999999998)\n Predict: 11.9\n Else (feature 7 > 1.1902499999999998)\n Predict: 21.63076923076923\n Else (feature 7 > 1.7616)\n If (feature 5 <= 6.1625)\n Predict: 15.147368421052633\n Else (feature 5 > 6.1625)\n Predict: 19.492857142857144\n Else (feature 4 > 0.6635)\n If (feature 11 <= 393.845)\n If (feature 12 <= 14.675)\n If (feature 5 <= 5.805)\n If (feature 11 <= 342.44)\n Predict: 20.75\n Else (feature 11 > 342.44)\n Predict: 16.049999999999997\n Else (feature 5 > 5.805)\n If (feature 4 <= 0.7155)\n Predict: 20.2\n Else (feature 4 > 0.7155)\n Predict: 22.228571428571428\n Else (feature 12 > 14.675)\n If (feature 12 <= 21.455)\n If (feature 11 <= 356.635)\n Predict: 14.218181818181819\n Else (feature 11 > 356.635)\n Predict: 19.299999999999997\n Else (feature 12 > 21.455)\n If (feature 6 <= 90.9)\n Predict: 7.65\n Else (feature 6 > 90.9)\n Predict: 11.136363636363637\n Else (feature 11 > 393.845)\n If (feature 0 <= 13.440999999999999)\n If (feature 6 <= 84.55)\n Predict: 20.0\n Else (feature 6 > 84.55)\n If (feature 7 <= 1.4123999999999999)\n Predict: 13.766666666666666\n Else (feature 7 > 1.4123999999999999)\n Predict: 12.65\n Else (feature 0 > 13.440999999999999)\n If (feature 5 <= 4.9045)\n If (feature 12 <= 28.665)\n Predict: 10.5\n Else (feature 12 > 28.665)\n Predict: 13.8\n Else (feature 5 > 4.9045)\n If (feature 12 <= 19.845)\n Predict: 9.0\n Else (feature 12 > 19.845)\n Predict: 6.4\n'
In [29]:
model.stages[1].save('BostonRF')
model_df = spark.read.parquet("BostonRF/data")
print(model_df.printSchema())
root
|-- treeID: integer (nullable = true)
|-- nodeData: struct (nullable = true)
| |-- id: integer (nullable = true)
| |-- prediction: double (nullable = true)
| |-- impurity: double (nullable = true)
| |-- impurityStats: array (nullable = true)
| | |-- element: double (containsNull = true)
| |-- gain: double (nullable = true)
| |-- leftChild: integer (nullable = true)
| |-- rightChild: integer (nullable = true)
| |-- split: struct (nullable = true)
| | |-- featureIndex: integer (nullable = true)
| | |-- leftCategoriesOrThreshold: array (nullable = true)
| | | |-- element: double (containsNull = true)
| | |-- numCategories: integer (nullable = true)
None
In [30]:
model_df.toJSON().collect()
Out[30]:
['{"treeID":0,"nodeData":{"id":0,"prediction":22.91264367816093,"impurity":94.69288611441378,"impurityStats":[348.0,7973.600000000003,215649.38000000003],"gain":56.2842968634724,"leftChild":1,"rightChild":30,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[6.941],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":1,"prediction":19.419580419580424,"impurity":32.360315907868106,"impurityStats":[286.0,5554.000000000001,117111.39999999997],"gain":17.357070701281835,"leftChild":2,"rightChild":15,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[14.399999999999999],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":2,"prediction":23.03865030674848,"impurity":13.047524558695835,"impurityStats":[163.0,3755.300000000002,88643.79000000002],"gain":5.329690268056726,"leftChild":3,"rightChild":8,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[6.543],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":3,"prediction":21.67851239669422,"impurity":7.343174646540282,"impurityStats":[121.0,2623.100000000001,57753.43],"gain":1.323313093951433,"leftChild":4,"rightChild":5,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[1.3034],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":4,"prediction":27.9,"impurity":0.0,"impurityStats":[4.0,111.6,3113.64],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":5,"prediction":21.46581196581197,"impurity":6.225668785155989,"impurityStats":[117.0,2511.5000000000005,54639.790000000015],"gain":0.580351546838787,"leftChild":6,"rightChild":7,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[9.350000000000001],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":6,"prediction":24.48571428571429,"impurity":0.11265306122433165,"impurityStats":[7.0,171.40000000000003,4197.64],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":7,"prediction":21.273636363636367,"impurity":5.997395867768566,"impurityStats":[110.0,2340.1000000000004,50442.15000000001],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":8,"prediction":26.957142857142884,"impurity":8.797210884352396,"impurityStats":[42.0,1132.2000000000012,30890.360000000008],"gain":2.4330499411286093,"leftChild":9,"rightChild":12,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[5.4009],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":9,"prediction":28.18076923076923,"impurity":5.965399408284261,"impurityStats":[26.0,732.6999999999999,20803.15],"gain":1.6265853057200448,"leftChild":10,"rightChild":11,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[41.3],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":10,"prediction":29.558333333333334,"impurity":3.8374305555554806,"impurityStats":[12.0,354.7,10530.39],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":11,"prediction":26.99999999999999,"impurity":4.768571428571704,"impurityStats":[14.0,377.99999999999983,10272.759999999995],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":12,"prediction":24.968749999999993,"impurity":7.012148437500514,"impurityStats":[16.0,399.4999999999999,10087.210000000003],"gain":2.690283358135673,"leftChild":13,"rightChild":14,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[30.950000000000003],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":13,"prediction":26.82857142857143,"impurity":6.782040816326506,"impurityStats":[7.0,187.8,5085.88],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":14,"prediction":23.522222222222226,"impurity":2.40839506172799,"impurityStats":[9.0,211.70000000000005,5001.329999999999],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":15,"prediction":14.623577235772364,"impurity":17.59497256923782,"impurityStats":[123.0,1798.7000000000007,28467.610000000015],"gain":8.325458934004823,"leftChild":16,"rightChild":23,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[2.0754],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":16,"prediction":12.07101449275362,"impurity":10.835391724427767,"impurityStats":[69.0,832.8999999999997,10801.590000000002],"gain":3.547249085221071,"leftChild":17,"rightChild":20,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[4.2418499999999995],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":17,"prediction":15.126315789473686,"impurity":3.3850969529084978,"impurityStats":[19.0,287.40000000000003,4411.62],"gain":0.9573776546629529,"leftChild":18,"rightChild":19,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[1.3513000000000002],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":18,"prediction":12.866666666666667,"impurity":0.5688888888888831,"impurityStats":[3.0,38.6,498.36],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":19,"prediction":15.550000000000002,"impurity":2.7762499999999193,"impurityStats":[16.0,248.80000000000004,3913.2599999999998],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":20,"prediction":10.91,"impurity":8.77130000000001,"impurityStats":[50.0,545.5,6389.97],"gain":2.6931852049910865,"leftChild":21,"rightChild":22,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[23.835],"numCategories":-1}}}',
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'{"treeID":18,"nodeData":{"id":43,"prediction":50.0,"impurity":0.0,"impurityStats":[3.0,150.0,7500.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":18,"nodeData":{"id":44,"prediction":50.0,"impurity":0.0,"impurityStats":[5.0,250.0,12500.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":0,"prediction":22.948031496062995,"impurity":76.84680058693453,"impurityStats":[381.0,8743.2,229917.86000000004],"gain":19.39768884712319,"leftChild":1,"rightChild":30,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[0.6635],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":1,"prediction":24.982484076433128,"impurity":64.47966134528751,"impurityStats":[314.0,7844.500000000002,216221.71],"gain":28.79607459008176,"leftChild":2,"rightChild":15,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[4.475],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":2,"prediction":40.91249999999999,"impurity":62.74359375000108,"impurityStats":[32.0,1309.1999999999996,55570.44],"gain":24.970062003969133,"leftChild":3,"rightChild":8,"split":{"featureIndex":1,"leftCategoriesOrThreshold":[6.25],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":3,"prediction":46.57857142857143,"impurity":20.2702551020406,"impurityStats":[14.0,652.1,30657.67],"gain":8.779630102040887,"leftChild":4,"rightChild":7,"split":{"featureIndex":2,"leftCategoriesOrThreshold":[6.305],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":4,"prediction":44.0125,"impurity":20.1085937499995,"impurityStats":[8.0,352.1,15657.669999999998],"gain":15.276260416666105,"leftChild":5,"rightChild":6,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[0.339945],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":5,"prediction":47.04,"impurity":5.490399999999863,"impurityStats":[5.0,235.2,11091.259999999998],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":6,"prediction":38.96666666666666,"impurity":3.7355555555559476,"impurityStats":[3.0,116.89999999999998,4566.41],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":7,"prediction":50.0,"impurity":0.0,"impurityStats":[6.0,300.0,15000.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":8,"prediction":36.505555555555546,"impurity":51.38719135802522,"impurityStats":[18.0,657.0999999999998,24912.769999999997],"gain":41.35445987654404,"leftChild":9,"rightChild":12,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[7.33],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":9,"prediction":31.958333333333332,"impurity":10.089097222222032,"impurityStats":[12.0,383.5,12377.089999999998],"gain":4.8347638888888005,"leftChild":10,"rightChild":11,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[6.9625],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":10,"prediction":30.1,"impurity":6.437142857142915,"impurityStats":[7.0,210.70000000000002,6387.130000000001],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":11,"prediction":34.559999999999995,"impurity":3.598399999999674,"impurityStats":[5.0,172.79999999999998,5989.959999999997],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":12,"prediction":45.6,"impurity":9.919999999999467,"impurityStats":[6.0,273.6,12535.679999999998],"gain":9.679999999999685,"leftChild":13,"rightChild":14,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[0.015195],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":13,"prediction":50.0,"impurity":0.0,"impurityStats":[2.0,100.0,5000.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":14,"prediction":43.400000000000006,"impurity":0.3599999999996726,"impurityStats":[4.0,173.60000000000002,7535.68],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":15,"prediction":23.17482269503547,"impurity":32.61294766359777,"impurityStats":[282.0,6535.300000000003,160651.26999999996],"gain":5.418152681934397,"leftChild":16,"rightChild":23,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[93.15],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":16,"prediction":24.229059829059832,"impurity":28.76915552633522,"impurityStats":[234.0,5669.6,144101.06000000006],"gain":9.47052970770929,"leftChild":17,"rightChild":20,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[8.075],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":17,"prediction":28.086813186813178,"impurity":34.43740852554086,"impurityStats":[91.0,2555.899999999999,74920.89],"gain":17.393592661917012,"leftChild":18,"rightChild":19,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[6.797],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":18,"prediction":24.474999999999998,"impurity":10.858413461538545,"impurityStats":[52.0,1272.6999999999998,31713.969999999998],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":19,"prediction":32.9025641025641,"impurity":25.29101906640426,"impurityStats":[39.0,1283.1999999999998,43206.92000000001],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":20,"prediction":21.774125874125883,"impurity":9.664855005134608,"impurityStats":[143.0,3113.700000000001,69180.17000000003],"gain":1.3313802190512358,"leftChild":21,"rightChild":22,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[14.399999999999999],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":21,"prediction":22.343478260869563,"impurity":8.978805293005754,"impurityStats":[115.0,2569.5,58444.13000000001],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":22,"prediction":19.435714285714294,"impurity":5.683010204080737,"impurityStats":[28.0,544.2000000000003,10736.039999999986],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":23,"prediction":18.03541666666668,"impurity":19.519787326388155,"impurityStats":[48.0,865.7000000000007,16550.209999999992],"gain":2.4001913667922654,"leftChild":24,"rightChild":27,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[1.7616],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":24,"prediction":20.333333333333332,"impurity":26.71555555555557,"impurityStats":[15.0,305.0,6602.400000000001],"gain":10.941709401709339,"leftChild":25,"rightChild":26,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[1.1902499999999998],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":25,"prediction":11.9,"impurity":0.0,"impurityStats":[2.0,23.8,283.22],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":26,"prediction":21.63076923076923,"impurity":18.200591715976422,"impurityStats":[13.0,281.2,6319.18],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":27,"prediction":16.990909090909092,"impurity":12.757796143250578,"impurityStats":[33.0,560.7,9947.809999999998],"gain":4.612442987634385,"leftChild":28,"rightChild":29,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[6.1625],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":28,"prediction":15.147368421052633,"impurity":4.483545706371125,"impurityStats":[19.0,287.8,4444.599999999999],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":29,"prediction":19.492857142857144,"impurity":13.114948979591645,"impurityStats":[14.0,272.90000000000003,5503.209999999998],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":30,"prediction":13.41343283582088,"impurity":24.499968812654366,"impurityStats":[67.0,898.6999999999989,13696.150000000052],"gain":4.3057799072751335,"leftChild":31,"rightChild":46,"split":{"featureIndex":11,"leftCategoriesOrThreshold":[393.845],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":31,"prediction":15.06585365853659,"impurity":24.459321832242583,"impurityStats":[41.0,617.7000000000002,10309.01],"gain":14.940008645429415,"leftChild":32,"rightChild":39,"split":{"featureIndex":12,"leftCategoriesOrThreshold":[14.675],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":32,"prediction":20.73846153846154,"impurity":5.223905325443621,"impurityStats":[13.0,269.6,5658.999999999999],"gain":2.430401051939375,"leftChild":33,"rightChild":36,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[5.805],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":33,"prediction":18.400000000000002,"impurity":6.464999999999975,"impurityStats":[4.0,73.60000000000001,1380.1000000000001],"gain":5.522499999999894,"leftChild":34,"rightChild":35,"split":{"featureIndex":11,"leftCategoriesOrThreshold":[342.44],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":34,"prediction":20.75,"impurity":1.322499999999991,"impurityStats":[2.0,41.5,863.77],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":35,"prediction":16.049999999999997,"impurity":0.5625000000001705,"impurityStats":[2.0,32.099999999999994,516.3300000000002],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":36,"prediction":21.77777777777778,"impurity":1.1617283950616992,"impurityStats":[9.0,196.0,4278.9],"gain":0.7112522045854854,"leftChild":37,"rightChild":38,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[0.7155],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":37,"prediction":20.2,"impurity":0.0,"impurityStats":[2.0,40.4,816.0799999999999],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":19,"nodeData":{"id":38,"prediction":22.228571428571428,"impurity":0.5791836734694178,"impurityStats":[7.0,155.6,3462.8199999999997],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
...]
In [31]:
predictions = model.transform(vtest_df)
# Evaluate model on test instances and compute test error
predictions.select("prediction","MV","features").show(5)
from pyspark.ml.evaluation import RegressionEvaluator
evaluator = RegressionEvaluator(predictionCol="prediction", \
labelCol="MV",metricName="r2")
print("R Squared (R2) on test data = %g" % evaluator.evaluate(predictions))
+------------------+----+--------------------+
| prediction| MV| features|
+------------------+----+--------------------+
| 22.38020383081665|23.6|[0.09178,0.0,4.05...|
|30.574195958330176|32.4|[0.05644,40.0,6.4...|
|17.115010548254187|13.6|[0.10574,0.0,27.7...|
| 24.57515413490858|22.8|[0.09164,0.0,10.8...|
|17.458679803507316|16.1|[5.09017,0.0,18.1...|
+------------------+----+--------------------+
only showing top 5 rows
R Squared (R2) on test data = 0.799972
In [32]:
type(model.stages[1])
Out[32]:
pyspark.ml.regression.RandomForestRegressionModel
In [33]:
from catboost.datasets import amazon
In [34]:
train_df = sqlContext.createDataFrame(amazon()[0])
train_df.show(5)
+------+--------+------+-------------+-------------+-------------+----------+----------------+-----------+---------+
|ACTION|RESOURCE|MGR_ID|ROLE_ROLLUP_1|ROLE_ROLLUP_2|ROLE_DEPTNAME|ROLE_TITLE|ROLE_FAMILY_DESC|ROLE_FAMILY|ROLE_CODE|
+------+--------+------+-------------+-------------+-------------+----------+----------------+-----------+---------+
| 1| 39353| 85475| 117961| 118300| 123472| 117905| 117906| 290919| 117908|
| 1| 17183| 1540| 117961| 118343| 123125| 118536| 118536| 308574| 118539|
| 1| 36724| 14457| 118219| 118220| 117884| 117879| 267952| 19721| 117880|
| 1| 36135| 5396| 117961| 118343| 119993| 118321| 240983| 290919| 118322|
| 1| 42680| 5905| 117929| 117930| 119569| 119323| 123932| 19793| 119325|
+------+--------+------+-------------+-------------+-------------+----------+----------------+-----------+---------+
only showing top 5 rows
In [35]:
test_df = sqlContext.createDataFrame(amazon()[1])
test_df.show(5)
+---+--------+------+-------------+-------------+-------------+----------+----------------+-----------+---------+
| id|RESOURCE|MGR_ID|ROLE_ROLLUP_1|ROLE_ROLLUP_2|ROLE_DEPTNAME|ROLE_TITLE|ROLE_FAMILY_DESC|ROLE_FAMILY|ROLE_CODE|
+---+--------+------+-------------+-------------+-------------+----------+----------------+-----------+---------+
| 1| 78766| 72734| 118079| 118080| 117878| 117879| 118177| 19721| 117880|
| 2| 40644| 4378| 117961| 118327| 118507| 118863| 122008| 118398| 118865|
| 3| 75443| 2395| 117961| 118300| 119488| 118172| 301534| 249618| 118175|
| 4| 43219| 19986| 117961| 118225| 118403| 120773| 136187| 118960| 120774|
| 5| 42093| 50015| 117961| 118343| 119598| 118422| 300136| 118424| 118425|
+---+--------+------+-------------+-------------+-------------+----------+----------------+-----------+---------+
only showing top 5 rows
In [36]:
vectorAssembler = VectorAssembler(inputCols = test_df.drop("id").columns, outputCol = 'features')
vtrain_df = vectorAssembler.transform(train_df)
vtrain_df = vtrain_df.select(['features', 'ACTION'])
vtrain_df.show(3)
+--------------------+------+
| features|ACTION|
+--------------------+------+
|[39353.0,85475.0,...| 1|
|[17183.0,1540.0,1...| 1|
|[36724.0,14457.0,...| 1|
+--------------------+------+
only showing top 3 rows
In [37]:
featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(vtrain_df)
dtr = DecisionTreeClassifier(featuresCol="indexedFeatures", labelCol="ACTION", maxDepth=5, maxBins=255)
pipeline = Pipeline(stages=[featureIndexer, dtr])
model = pipeline.fit(vtrain_df)
In [38]:
print(model.stages[1].numNodes)
print(model.stages[1].featureImportances)
59
(9,[0,1,2,3,4,5,6,7,8],[0.006310731941243834,0.10373891385615842,0.07785150458406936,0.3016842901766434,0.10240395894163831,0.0054805511948246,0.172792142845202,0.1391329168221278,0.09060498963809237])
In [39]:
model.stages[1].toDebugString
Out[39]:
'DecisionTreeClassificationModel (uid=DecisionTreeClassifier_434880f939812808a5e5) of depth 5 with 59 nodes\n If (feature 7 <= 68840.0)\n If (feature 3 <= 117961.0)\n If (feature 0 <= 38391.5)\n If (feature 1 <= 50586.5)\n If (feature 2 <= 118076.5)\n Predict: 1.0\n Else (feature 2 > 118076.5)\n Predict: 1.0\n Else (feature 1 > 50586.5)\n If (feature 1 <= 51021.5)\n Predict: 0.0\n Else (feature 1 > 51021.5)\n Predict: 1.0\n Else (feature 0 > 38391.5)\n If (feature 2 <= 117939.0)\n If (feature 0 <= 81527.5)\n Predict: 1.0\n Else (feature 0 > 81527.5)\n Predict: 1.0\n Else (feature 2 > 117939.0)\n If (feature 4 <= 117943.0)\n Predict: 1.0\n Else (feature 4 > 117943.0)\n Predict: 1.0\n Else (feature 3 > 117961.0)\n If (feature 6 <= 90233.0)\n If (feature 1 <= 1312.0)\n Predict: 1.0\n Else (feature 1 > 1312.0)\n Predict: 0.0\n Else (feature 6 > 90233.0)\n If (feature 3 <= 118591.5)\n If (feature 6 <= 131852.5)\n Predict: 1.0\n Else (feature 6 > 131852.5)\n Predict: 1.0\n Else (feature 3 > 118591.5)\n If (feature 4 <= 132147.5)\n Predict: 1.0\n Else (feature 4 > 132147.5)\n Predict: 0.0\n Else (feature 7 > 68840.0)\n If (feature 7 <= 291857.0)\n If (feature 6 <= 117909.5)\n If (feature 8 <= 118287.5)\n If (feature 3 <= 120349.0)\n Predict: 1.0\n Else (feature 3 > 120349.0)\n Predict: 1.0\n Else (feature 8 > 118287.5)\n If (feature 3 <= 118064.0)\n Predict: 1.0\n Else (feature 3 > 118064.0)\n Predict: 1.0\n Else (feature 6 > 117909.5)\n If (feature 6 <= 311461.5)\n If (feature 3 <= 118295.5)\n Predict: 1.0\n Else (feature 3 > 118295.5)\n Predict: 1.0\n Else (feature 6 > 311461.5)\n If (feature 8 <= 118348.0)\n Predict: 1.0\n Else (feature 8 > 118348.0)\n Predict: 1.0\n Else (feature 7 > 291857.0)\n If (feature 2 <= 117968.0)\n If (feature 1 <= 876.5)\n If (feature 4 <= 118532.0)\n Predict: 0.0\n Else (feature 4 > 118532.0)\n Predict: 1.0\n Else (feature 1 > 876.5)\n If (feature 5 <= 118693.5)\n Predict: 1.0\n Else (feature 5 > 118693.5)\n Predict: 1.0\n Else (feature 2 > 117968.0)\n If (feature 3 <= 118261.5)\n If (feature 6 <= 280904.0)\n Predict: 1.0\n Else (feature 6 > 280904.0)\n Predict: 0.0\n Else (feature 3 > 118261.5)\n If (feature 6 <= 117946.5)\n Predict: 1.0\n Else (feature 6 > 117946.5)\n Predict: 1.0\n'
In [40]:
dtr.getProbabilityCol()
Out[40]:
'probability'
In [41]:
model.stages[1].predictionCol
Out[41]:
Param(parent='DecisionTreeClassifier_434880f939812808a5e5', name='predictionCol', doc='prediction column name')
In [42]:
categoricalFeatures = featureIndexer.categoryMaps
print("Chose %d categorical features: %s" %
(len(categoricalFeatures), ", ".join(str(k) for k in categoricalFeatures.keys())))
Chose 0 categorical features:
In [43]:
model.stages[1].save('AmazonDT')
model_df = spark.read.parquet("AmazonDT/data")
print(model_df.printSchema())
model_df.toJSON().collect()
root
|-- id: integer (nullable = true)
|-- prediction: double (nullable = true)
|-- impurity: double (nullable = true)
|-- impurityStats: array (nullable = true)
| |-- element: double (containsNull = true)
|-- gain: double (nullable = true)
|-- leftChild: integer (nullable = true)
|-- rightChild: integer (nullable = true)
|-- split: struct (nullable = true)
| |-- featureIndex: integer (nullable = true)
| |-- leftCategoriesOrThreshold: array (nullable = true)
| | |-- element: double (containsNull = true)
| |-- numCategories: integer (nullable = true)
None
Out[43]:
['{"id":0,"prediction":1.0,"impurity":0.10907763557415207,"impurityStats":[1897.0,30872.0],"gain":4.263583503495705E-4,"leftChild":1,"rightChild":28,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[68840.0],"numCategories":-1}}',
'{"id":1,"prediction":1.0,"impurity":0.17885170197832712,"impurityStats":[360.0,3266.0],"gain":0.002209248674808345,"leftChild":2,"rightChild":17,"split":{"featureIndex":3,"leftCategoriesOrThreshold":[117961.0],"numCategories":-1}}',
'{"id":2,"prediction":1.0,"impurity":0.08954551596774918,"impurityStats":[49.0,994.0],"gain":9.793332982563647E-4,"leftChild":3,"rightChild":10,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[38391.5],"numCategories":-1}}',
'{"id":3,"prediction":1.0,"impurity":0.11672325042162734,"impurityStats":[44.0,663.0],"gain":0.0013809294282496887,"leftChild":4,"rightChild":7,"split":{"featureIndex":1,"leftCategoriesOrThreshold":[50586.5],"numCategories":-1}}',
'{"id":4,"prediction":1.0,"impurity":0.08105118526197796,"impurityStats":[19.0,430.0],"gain":0.002318336818661818,"leftChild":5,"rightChild":6,"split":{"featureIndex":2,"leftCategoriesOrThreshold":[118076.5],"numCategories":-1}}',
'{"id":5,"prediction":1.0,"impurity":0.0674849625898577,"impurityStats":[15.0,414.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":6,"prediction":1.0,"impurity":0.31999999999999984,"impurityStats":[4.0,16.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":7,"prediction":1.0,"impurity":0.17501953007631743,"impurityStats":[25.0,233.0],"gain":0.02126025929675568,"leftChild":8,"rightChild":9,"split":{"featureIndex":1,"leftCategoriesOrThreshold":[51021.5],"numCategories":-1}}',
'{"id":8,"prediction":0.0,"impurity":0.40816326530612246,"impurityStats":[5.0,2.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":9,"prediction":1.0,"impurity":0.14666433866129114,"impurityStats":[20.0,231.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":10,"prediction":1.0,"impurity":0.029319019274376412,"impurityStats":[5.0,331.0],"gain":9.444449717326622E-4,"leftChild":11,"rightChild":14,"split":{"featureIndex":2,"leftCategoriesOrThreshold":[117939.0],"numCategories":-1}}',
'{"id":11,"prediction":1.0,"impurity":0.013467394426888823,"impurityStats":[2.0,293.0],"gain":2.717087566268912E-4,"leftChild":12,"rightChild":13,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[81527.5],"numCategories":-1}}',
'{"id":12,"prediction":1.0,"impurity":0.007246280991735521,"impurityStats":[1.0,274.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":13,"prediction":1.0,"impurity":0.09500000000000008,"impurityStats":[1.0,19.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":14,"prediction":1.0,"impurity":0.1356335514574658,"impurityStats":[3.0,38.0],"gain":0.015160528545418606,"leftChild":15,"rightChild":16,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[117943.0],"numCategories":-1}}',
'{"id":15,"prediction":1.0,"impurity":0.058769513314967714,"impurityStats":[1.0,32.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":16,"prediction":1.0,"impurity":0.375,"impurityStats":[2.0,6.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":17,"prediction":1.0,"impurity":0.21181167732256934,"impurityStats":[311.0,2272.0],"gain":0.002022374194227572,"leftChild":18,"rightChild":21,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[90233.0],"numCategories":-1}}',
'{"id":18,"prediction":0.0,"impurity":0.5,"impurityStats":[9.0,9.0],"gain":0.5,"leftChild":19,"rightChild":20,"split":{"featureIndex":1,"leftCategoriesOrThreshold":[1312.0],"numCategories":-1}}',
'{"id":19,"prediction":1.0,"impurity":0.0,"impurityStats":[0.0,9.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":20,"prediction":0.0,"impurity":0.0,"impurityStats":[9.0,0.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":21,"prediction":1.0,"impurity":0.20775273683450557,"impurityStats":[302.0,2263.0],"gain":0.0017366548760888306,"leftChild":22,"rightChild":25,"split":{"featureIndex":3,"leftCategoriesOrThreshold":[118591.5],"numCategories":-1}}',
'{"id":22,"prediction":1.0,"impurity":0.19125232282662374,"impurityStats":[243.0,2026.0],"gain":0.001598373397153352,"leftChild":23,"rightChild":24,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[131852.5],"numCategories":-1}}',
'{"id":23,"prediction":1.0,"impurity":0.23063509635339074,"impurityStats":[164.0,1069.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":24,"prediction":1.0,"impurity":0.1408800554553451,"impurityStats":[79.0,957.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":25,"prediction":1.0,"impurity":0.31918827611395173,"impurityStats":[59.0,237.0],"gain":0.020452737601461567,"leftChild":26,"rightChild":27,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[132147.5],"numCategories":-1}}',
'{"id":26,"prediction":1.0,"impurity":0.2786031149376045,"impurityStats":[45.0,224.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":27,"prediction":0.0,"impurity":0.49931412894375865,"impurityStats":[14.0,13.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":28,"prediction":1.0,"impurity":0.09991687307323782,"impurityStats":[1537.0,27606.0],"gain":3.5396653250259653E-4,"leftChild":29,"rightChild":44,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[291857.0],"numCategories":-1}}',
'{"id":29,"prediction":1.0,"impurity":0.09242526742516133,"impurityStats":[1289.0,25249.0],"gain":2.0419325961274631E-4,"leftChild":30,"rightChild":37,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[117909.5],"numCategories":-1}}',
'{"id":30,"prediction":1.0,"impurity":0.12213666258118816,"impurityStats":[462.0,6609.0],"gain":0.0016920270119335362,"leftChild":31,"rightChild":34,"split":{"featureIndex":8,"leftCategoriesOrThreshold":[118287.5],"numCategories":-1}}',
'{"id":31,"prediction":1.0,"impurity":0.058631027550307246,"impurityStats":[87.0,2791.0],"gain":0.0038366677694229873,"leftChild":32,"rightChild":33,"split":{"featureIndex":3,"leftCategoriesOrThreshold":[120349.0],"numCategories":-1}}',
'{"id":32,"prediction":1.0,"impurity":0.04643696185578472,"impurityStats":[67.0,2750.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":33,"prediction":1.0,"impurity":0.4407417360924483,"impurityStats":[20.0,41.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":34,"prediction":1.0,"impurity":0.16287238750785005,"impurityStats":[375.0,3818.0],"gain":0.0030467468451516877,"leftChild":35,"rightChild":36,"split":{"featureIndex":3,"leftCategoriesOrThreshold":[118064.0],"numCategories":-1}}',
'{"id":35,"prediction":1.0,"impurity":0.2898000280898321,"impurityStats":[125.0,586.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":36,"prediction":1.0,"impurity":0.13328578154130488,"impurityStats":[250.0,3232.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":37,"prediction":1.0,"impurity":0.08135483254192977,"impurityStats":[827.0,18640.0],"gain":9.655755276598071E-5,"leftChild":38,"rightChild":41,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[311461.5],"numCategories":-1}}',
'{"id":38,"prediction":1.0,"impurity":0.07981731585000529,"impurityStats":[799.0,18388.0],"gain":1.0260450188539355E-4,"leftChild":39,"rightChild":40,"split":{"featureIndex":3,"leftCategoriesOrThreshold":[118295.5],"numCategories":-1}}',
'{"id":39,"prediction":1.0,"impurity":0.09699467465050637,"impurityStats":[357.0,6628.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":40,"prediction":1.0,"impurity":0.06982284577959263,"impurityStats":[442.0,11760.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":41,"prediction":1.0,"impurity":0.17999999999999994,"impurityStats":[28.0,252.0],"gain":0.013755878143528855,"leftChild":42,"rightChild":43,"split":{"featureIndex":8,"leftCategoriesOrThreshold":[118348.0],"numCategories":-1}}',
'{"id":42,"prediction":1.0,"impurity":0.3035197148585431,"impurityStats":[25.0,109.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":43,"prediction":1.0,"impurity":0.040251454306624135,"impurityStats":[3.0,143.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":44,"prediction":1.0,"impurity":0.1722764062908697,"impurityStats":[248.0,2357.0],"gain":0.004695377793759226,"leftChild":45,"rightChild":52,"split":{"featureIndex":2,"leftCategoriesOrThreshold":[117968.0],"numCategories":-1}}',
'{"id":45,"prediction":1.0,"impurity":0.10292803009943574,"impurityStats":[83.0,1442.0],"gain":0.00173584076356606,"leftChild":46,"rightChild":49,"split":{"featureIndex":1,"leftCategoriesOrThreshold":[876.5],"numCategories":-1}}',
'{"id":46,"prediction":1.0,"impurity":0.31999999999999984,"impurityStats":[12.0,48.0],"gain":0.1866666666666665,"leftChild":47,"rightChild":48,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[118532.0],"numCategories":-1}}',
'{"id":47,"prediction":0.0,"impurity":0.4444444444444445,"impurityStats":[12.0,6.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":48,"prediction":1.0,"impurity":0.0,"impurityStats":[0.0,42.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":49,"prediction":1.0,"impurity":0.09223077729501794,"impurityStats":[71.0,1394.0],"gain":6.530257415538337E-4,"leftChild":50,"rightChild":51,"split":{"featureIndex":5,"leftCategoriesOrThreshold":[118693.5],"numCategories":-1}}',
'{"id":50,"prediction":1.0,"impurity":0.060546875,"impurityStats":[24.0,744.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":51,"prediction":1.0,"impurity":0.12576959257650633,"impurityStats":[47.0,650.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":52,"prediction":1.0,"impurity":0.2588734567901234,"impurityStats":[165.0,915.0],"gain":0.013343532786129306,"leftChild":53,"rightChild":56,"split":{"featureIndex":3,"leftCategoriesOrThreshold":[118261.5],"numCategories":-1}}',
'{"id":53,"prediction":1.0,"impurity":0.38031225613889186,"impurityStats":[107.0,312.0],"gain":0.027903656223209332,"leftChild":54,"rightChild":55,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[280904.0],"numCategories":-1}}',
'{"id":54,"prediction":1.0,"impurity":0.3380347442281576,"impurityStats":[81.0,295.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":55,"prediction":0.0,"impurity":0.47809626825310986,"impurityStats":[26.0,17.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":56,"prediction":1.0,"impurity":0.16009301452665348,"impurityStats":[58.0,603.0],"gain":0.0035125921841863417,"leftChild":57,"rightChild":58,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[117946.5],"numCategories":-1}}',
'{"id":57,"prediction":1.0,"impurity":0.49382716049382713,"impurityStats":[4.0,5.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}',
'{"id":58,"prediction":1.0,"impurity":0.15192517595694222,"impurityStats":[54.0,598.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}']
In [44]:
gbt = GBTClassifier(featuresCol="indexedFeatures", labelCol="ACTION", maxDepth=5, maxBins=255)
pipeline = Pipeline(stages=[featureIndexer, gbt])
model = pipeline.fit(vtrain_df)
In [45]:
print(model.stages[1].getNumTrees)
print(model.stages[1].featureImportances)
20
(9,[0,1,2,3,4,5,6,7,8],[0.13554215701265498,0.17607580800574502,0.07144200907553364,0.13379837393009836,0.1591850279937512,0.08025501638404647,0.12839284324354233,0.0725143503683328,0.04279441398629514])
In [46]:
model.stages[1].toDebugString
Out[46]:
'GBTClassificationModel (uid=GBTClassifier_40dcb3876902378498c5) with 20 trees\n Tree 0 (weight 1.0):\n If (feature 7 <= 68840.0)\n If (feature 3 <= 117961.0)\n If (feature 0 <= 38391.5)\n If (feature 1 <= 50586.5)\n If (feature 2 <= 118076.5)\n Predict: 0.9300699300699301\n Else (feature 2 > 118076.5)\n Predict: 0.6\n Else (feature 1 > 50586.5)\n If (feature 1 <= 51021.5)\n Predict: -0.42857142857142855\n Else (feature 1 > 51021.5)\n Predict: 0.8406374501992032\n Else (feature 0 > 38391.5)\n If (feature 2 <= 117939.0)\n If (feature 0 <= 81527.5)\n Predict: 0.9927272727272727\n Else (feature 0 > 81527.5)\n Predict: 0.9\n Else (feature 2 > 117939.0)\n If (feature 4 <= 117943.0)\n Predict: 0.9393939393939394\n Else (feature 4 > 117943.0)\n Predict: 0.5\n Else (feature 3 > 117961.0)\n If (feature 6 <= 90233.0)\n If (feature 1 <= 1312.0)\n Predict: 1.0\n Else (feature 1 > 1312.0)\n Predict: -1.0\n Else (feature 6 > 90233.0)\n If (feature 3 <= 118591.5)\n If (feature 6 <= 131852.5)\n Predict: 0.7339821573398215\n Else (feature 6 > 131852.5)\n Predict: 0.8474903474903475\n Else (feature 3 > 118591.5)\n If (feature 4 <= 132147.5)\n Predict: 0.6654275092936803\n Else (feature 4 > 132147.5)\n Predict: -0.037037037037037035\n Else (feature 7 > 68840.0)\n If (feature 7 <= 291857.0)\n If (feature 6 <= 117909.5)\n If (feature 8 <= 118287.5)\n If (feature 3 <= 120349.0)\n Predict: 0.9524316648917288\n Else (feature 3 > 120349.0)\n Predict: 0.3442622950819672\n Else (feature 8 > 118287.5)\n If (feature 3 <= 118064.0)\n Predict: 0.6483825597749648\n Else (feature 3 > 118064.0)\n Predict: 0.8564043653072947\n Else (feature 6 > 117909.5)\n If (feature 6 <= 311461.5)\n If (feature 3 <= 118295.5)\n Predict: 0.897780959198282\n Else (feature 3 > 118295.5)\n Predict: 0.9275528601868546\n Else (feature 6 > 311461.5)\n If (feature 8 <= 118348.0)\n Predict: 0.6268656716417911\n Else (feature 8 > 118348.0)\n Predict: 0.958904109589041\n Else (feature 7 > 291857.0)\n If (feature 2 <= 117968.0)\n If (feature 1 <= 876.5)\n If (feature 4 <= 118532.0)\n Predict: -0.3333333333333333\n Else (feature 4 > 118532.0)\n Predict: 1.0\n Else (feature 1 > 876.5)\n If (feature 5 <= 118693.5)\n Predict: 0.9375\n Else (feature 5 > 118693.5)\n Predict: 0.8651362984218077\n Else (feature 2 > 117968.0)\n If (feature 3 <= 118261.5)\n If (feature 6 <= 280904.0)\n Predict: 0.5691489361702128\n Else (feature 6 > 280904.0)\n Predict: -0.20930232558139536\n Else (feature 3 > 118261.5)\n If (feature 6 <= 117946.5)\n Predict: 0.1111111111111111\n Else (feature 6 > 117946.5)\n Predict: 0.8343558282208589\n Tree 1 (weight 0.1):\n If (feature 1 <= 13837.0)\n If (feature 2 <= 118763.0)\n If (feature 3 <= 118333.5)\n If (feature 2 <= 118279.5)\n If (feature 1 <= 1590.0)\n Predict: 0.4837706917947696\n Else (feature 1 > 1590.0)\n Predict: 0.3697429767383776\n Else (feature 2 > 118279.5)\n If (feature 4 <= 120240.5)\n Predict: 0.2760218730073546\n Else (feature 4 > 120240.5)\n Predict: -0.35508035965494295\n Else (feature 3 > 118333.5)\n If (feature 4 <= 17949.0)\n If (feature 0 <= 79106.5)\n Predict: 0.289712017892\n Else (feature 0 > 79106.5)\n Predict: -1.4172355840311717\n Else (feature 4 > 17949.0)\n If (feature 3 <= 181079.0)\n Predict: 0.45664254165654683\n Else (feature 3 > 181079.0)\n Predict: -2.662554536632797\n Else (feature 2 > 118763.0)\n If (feature 4 <= 150387.0)\n If (feature 5 <= 118183.0)\n If (feature 0 <= 29292.5)\n Predict: 0.4514569309149842\n Else (feature 0 > 29292.5)\n Predict: -0.32368817113893517\n Else (feature 5 > 118183.0)\n If (feature 6 <= 223721.0)\n Predict: 0.45973077409213037\n Else (feature 6 > 223721.0)\n Predict: 0.14955048062506318\n Else (feature 4 > 150387.0)\n If (feature 0 <= 23981.5)\n If (feature 5 <= 120800.5)\n Predict: 0.24606574867643197\n Else (feature 5 > 120800.5)\n Predict: -1.9259597775273969\n Else (feature 0 > 23981.5)\n If (feature 1 <= 86.5)\n Predict: -0.4295668978074452\n Else (feature 1 > 86.5)\n Predict: -2.182069054245331\n Else (feature 1 > 13837.0)\n If (feature 1 <= 74189.5)\n If (feature 1 <= 71211.5)\n If (feature 2 <= 120030.0)\n If (feature 3 <= 119066.5)\n Predict: 0.31155451293280967\n Else (feature 3 > 119066.5)\n Predict: 0.1379867993203134\n Else (feature 2 > 120030.0)\n If (feature 5 <= 117905.5)\n Predict: 0.971913433858712\n Else (feature 5 > 117905.5)\n Predict: 0.5163981901841316\n Else (feature 1 > 71211.5)\n If (feature 3 <= 118218.5)\n If (feature 4 <= 119250.0)\n Predict: 0.5447484397903322\n Else (feature 4 > 119250.0)\n Predict: -0.09709799130814763\n Else (feature 3 > 118218.5)\n If (feature 6 <= 130151.5)\n Predict: -0.7374600948100323\n Else (feature 6 > 130151.5)\n Predict: 0.3150516623410601\n Else (feature 1 > 74189.5)\n If (feature 4 <= 117943.0)\n If (feature 1 <= 111810.5)\n If (feature 4 <= 117908.0)\n Predict: 0.5525263848513443\n Else (feature 4 > 117908.0)\n Predict: 0.19536274977262322\n Else (feature 1 > 111810.5)\n If (feature 3 <= 118078.0)\n Predict: 0.6024519411334713\n Else (feature 3 > 118078.0)\n Predict: -0.41402394594446124\n Else (feature 4 > 117943.0)\n If (feature 5 <= 117882.0)\n If (feature 1 <= 96501.5)\n Predict: 0.7490089012447125\n Else (feature 1 > 96501.5)\n Predict: -2.649126439060413\n Else (feature 5 > 117882.0)\n If (feature 8 <= 132683.5)\n Predict: 0.5018540539210025\n Else (feature 8 > 132683.5)\n Predict: 0.057756526240321904\n Tree 2 (weight 0.1):\n If (feature 1 <= 13837.0)\n If (feature 2 <= 118920.0)\n If (feature 3 <= 118333.5)\n If (feature 2 <= 118279.5)\n If (feature 8 <= 118805.0)\n Predict: 0.3216678763175871\n Else (feature 8 > 118805.0)\n Predict: 0.4108993561352557\n Else (feature 2 > 118279.5)\n If (feature 4 <= 20289.5)\n Predict: -1.1960650885767956\n Else (feature 4 > 20289.5)\n Predict: 0.15595659989286018\n Else (feature 3 > 118333.5)\n If (feature 4 <= 118633.0)\n If (feature 6 <= 301324.0)\n Predict: 0.3641433704357804\n Else (feature 6 > 301324.0)\n Predict: -0.1653735558923422\n Else (feature 4 > 118633.0)\n If (feature 3 <= 126510.5)\n Predict: 0.4278994314547867\n Else (feature 3 > 126510.5)\n Predict: -2.155697999942049\n Else (feature 2 > 118920.0)\n If (feature 4 <= 150387.0)\n If (feature 5 <= 118409.0)\n If (feature 5 <= 118369.0)\n Predict: 0.10203142438287849\n Else (feature 5 > 118369.0)\n Predict: -1.7885342413662308\n Else (feature 5 > 118409.0)\n If (feature 1 <= 13150.0)\n Predict: 0.389515050698072\n Else (feature 1 > 13150.0)\n Predict: -0.19637065538873028\n Else (feature 4 > 150387.0)\n If (feature 0 <= 23981.5)\n If (feature 2 <= 119008.5)\n Predict: -1.5486398259286016\n Else (feature 2 > 119008.5)\n Predict: 0.22301369643212537\n Else (feature 0 > 23981.5)\n If (feature 7 <= 118438.5)\n Predict: -1.9004831244477494\n Else (feature 7 > 118438.5)\n Predict: -0.3862850616669524\n Else (feature 1 > 13837.0)\n If (feature 1 <= 74189.5)\n If (feature 1 <= 71211.5)\n If (feature 2 <= 119322.0)\n If (feature 3 <= 119066.5)\n Predict: 0.2807244234695991\n Else (feature 3 > 119066.5)\n Predict: 0.040521981361209755\n Else (feature 2 > 119322.0)\n If (feature 4 <= 132147.5)\n Predict: 0.49102196092028294\n Else (feature 4 > 132147.5)\n Predict: -0.07715499926309093\n Else (feature 1 > 71211.5)\n If (feature 4 <= 118208.0)\n If (feature 6 <= 118165.0)\n Predict: 0.6167030168435421\n Else (feature 6 > 118165.0)\n Predict: 0.4106862924131473\n Else (feature 4 > 118208.0)\n If (feature 4 <= 118406.5)\n Predict: -1.521608664525078\n Else (feature 4 > 118406.5)\n Predict: -0.04964793693047253\n Else (feature 1 > 74189.5)\n If (feature 4 <= 117943.0)\n If (feature 1 <= 111810.5)\n If (feature 4 <= 117908.0)\n Predict: 0.49763515210474557\n Else (feature 4 > 117908.0)\n Predict: 0.17446823540346446\n Else (feature 1 > 111810.5)\n If (feature 4 <= 117930.5)\n Predict: 0.09358567163881892\n Else (feature 4 > 117930.5)\n Predict: -1.6856300732241154\n Else (feature 4 > 117943.0)\n If (feature 8 <= 117884.0)\n If (feature 1 <= 96501.5)\n Predict: 0.6620527338403449\n Else (feature 1 > 96501.5)\n Predict: -2.226455367502011\n Else (feature 8 > 117884.0)\n If (feature 8 <= 132683.5)\n Predict: 0.45423612936807894\n Else (feature 8 > 132683.5)\n Predict: 0.05223660829079435\n Tree 3 (weight 0.1):\n If (feature 0 <= 6970.5)\n If (feature 0 <= 3850.0)\n If (feature 7 <= 291857.0)\n If (feature 1 <= 1590.0)\n If (feature 6 <= 302769.0)\n Predict: 0.4760454212873163\n Else (feature 6 > 302769.0)\n Predict: 0.2759129154876251\n Else (feature 1 > 1590.0)\n If (feature 1 <= 22444.5)\n Predict: 0.2601225876027867\n Else (feature 1 > 22444.5)\n Predict: 0.40732116714987415\n Else (feature 7 > 291857.0)\n If (feature 4 <= 122009.5)\n If (feature 1 <= 3609.0)\n Predict: -0.5951396588612737\n Else (feature 1 > 3609.0)\n Predict: 0.32154667019810684\n Else (feature 4 > 122009.5)\n If (feature 3 <= 118308.0)\n Predict: 0.710484548743759\n Else (feature 3 > 118308.0)\n Predict: -3.48546103007342\n Else (feature 0 > 3850.0)\n If (feature 0 <= 4678.0)\n If (feature 0 <= 4674.5)\n If (feature 3 <= 119703.5)\n Predict: 0.44460802053045845\n Else (feature 3 > 119703.5)\n Predict: 0.05093297439788712\n Else (feature 0 > 4674.5)\n If (feature 5 <= 182600.5)\n Predict: 0.4959576675298199\n Else (feature 5 > 182600.5)\n Predict: -0.6378780728070979\n Else (feature 0 > 4678.0)\n If (feature 4 <= 118916.0)\n If (feature 7 <= 119202.5)\n Predict: 0.5219718749759882\n Else (feature 7 > 119202.5)\n Predict: 0.41130018150717684\n Else (feature 4 > 118916.0)\n If (feature 4 <= 119005.5)\n Predict: -0.7931942903340615\n Else (feature 4 > 119005.5)\n Predict: 0.312226942644216\n Else (feature 0 > 6970.5)\n If (feature 1 <= 13837.0)\n If (feature 2 <= 118920.0)\n If (feature 1 <= 7338.5)\n If (feature 1 <= 1590.0)\n Predict: 0.3873623434769583\n Else (feature 1 > 1590.0)\n Predict: 0.28881271430065864\n Else (feature 1 > 7338.5)\n If (feature 5 <= 119969.0)\n Predict: 0.36048596467757776\n Else (feature 5 > 119969.0)\n Predict: 0.4751112033725937\n Else (feature 2 > 118920.0)\n If (feature 4 <= 150387.0)\n If (feature 5 <= 118409.0)\n Predict: -0.05790731409665284\n Else (feature 5 > 118409.0)\n Predict: 0.3401462443311765\n Else (feature 4 > 150387.0)\n If (feature 0 <= 23981.5)\n Predict: 0.25278596518404356\n Else (feature 0 > 23981.5)\n Predict: -1.3185552616041625\n Else (feature 1 > 13837.0)\n If (feature 1 <= 74189.5)\n If (feature 0 <= 23903.0)\n If (feature 0 <= 20889.0)\n Predict: 0.1894812870803583\n Else (feature 0 > 20889.0)\n Predict: -0.15309257408192825\n Else (feature 0 > 23903.0)\n If (feature 6 <= 128574.5)\n Predict: 0.2168685689628017\n Else (feature 6 > 128574.5)\n Predict: 0.3056770264122244\n Else (feature 1 > 74189.5)\n If (feature 3 <= 118064.0)\n If (feature 3 <= 117972.5)\n Predict: 0.3944506964144592\n Else (feature 3 > 117972.5)\n Predict: 0.5214728029830995\n Else (feature 3 > 118064.0)\n If (feature 7 <= 68840.0)\n Predict: 0.03222170936691956\n Else (feature 7 > 68840.0)\n Predict: 0.37196660211971555\n Tree 4 (weight 0.1):\n If (feature 0 <= 6715.5)\n If (feature 0 <= 3850.0)\n If (feature 7 <= 291857.0)\n If (feature 1 <= 4078.5)\n If (feature 6 <= 311385.0)\n Predict: 0.3876312258935441\n Else (feature 6 > 311385.0)\n Predict: -0.5267680801693686\n Else (feature 1 > 4078.5)\n If (feature 1 <= 22444.5)\n Predict: 0.18114996753657203\n Else (feature 1 > 22444.5)\n Predict: 0.3715487809098231\n Else (feature 7 > 291857.0)\n If (feature 4 <= 122009.5)\n If (feature 1 <= 2227.0)\n Predict: -0.8530642140206111\n Else (feature 1 > 2227.0)\n Predict: 0.23018846505024168\n Else (feature 4 > 122009.5)\n If (feature 3 <= 118308.0)\n Predict: 0.6321753293721272\n Else (feature 3 > 118308.0)\n Predict: -3.0860469200847516\n Else (feature 0 > 3850.0)\n If (feature 0 <= 4678.0)\n If (feature 6 <= 90233.0)\n Predict: -0.5710059395890824\n Else (feature 6 > 90233.0)\n If (feature 0 <= 4674.5)\n Predict: 0.39813206172874066\n Else (feature 0 > 4674.5)\n Predict: 0.45030914358295243\n Else (feature 0 > 4678.0)\n If (feature 0 <= 4679.5)\n If (feature 1 <= 1021.0)\n Predict: 0.5042011806587203\n Else (feature 1 > 1021.0)\n Predict: -3.1137486616353653\n Else (feature 0 > 4679.5)\n If (feature 4 <= 122248.5)\n Predict: 0.38047445366511595\n Else (feature 4 > 122248.5)\n Predict: 0.16532574095303515\n Else (feature 0 > 6715.5)\n If (feature 1 <= 13837.0)\n If (feature 2 <= 118920.0)\n If (feature 3 <= 118333.5)\n If (feature 2 <= 118279.5)\n Predict: 0.28270284529436346\n Else (feature 2 > 118279.5)\n Predict: 0.0421476383705226\n Else (feature 3 > 118333.5)\n If (feature 4 <= 17949.0)\n Predict: -0.08826310405765328\n Else (feature 4 > 17949.0)\n Predict: 0.33968520036733174\n Else (feature 2 > 118920.0)\n If (feature 2 <= 120873.5)\n If (feature 3 <= 120874.5)\n Predict: 0.13737152396902044\n Else (feature 3 > 120874.5)\n Predict: -0.8749767820876916\n Else (feature 2 > 120873.5)\n If (feature 5 <= 117905.5)\n Predict: 1.2600943395343713\n Else (feature 5 > 117905.5)\n Predict: 0.3681948380565026\n Else (feature 1 > 13837.0)\n If (feature 1 <= 74189.5)\n If (feature 0 <= 22681.0)\n If (feature 0 <= 20889.0)\n Predict: 0.171696020264237\n Else (feature 0 > 20889.0)\n Predict: -0.47170322807353243\n Else (feature 0 > 22681.0)\n If (feature 7 <= 68840.0)\n Predict: 0.13772001719148336\n Else (feature 7 > 68840.0)\n Predict: 0.2549209229136697\n Else (feature 1 > 74189.5)\n If (feature 4 <= 117943.0)\n If (feature 3 <= 118025.0)\n Predict: 0.42062694392491684\n Else (feature 3 > 118025.0)\n Predict: 0.10683341486357463\n Else (feature 4 > 117943.0)\n If (feature 8 <= 132683.5)\n Predict: 0.37290585197553405\n Else (feature 8 > 132683.5)\n Predict: -0.03987184642787646\n Tree 5 (weight 0.1):\n If (feature 0 <= 6970.5)\n If (feature 0 <= 3850.0)\n If (feature 7 <= 291857.0)\n If (feature 1 <= 2008.5)\n If (feature 6 <= 309207.0)\n Predict: 0.39339804693031083\n Else (feature 6 > 309207.0)\n Predict: -3.5052242242460636\n Else (feature 1 > 2008.5)\n If (feature 1 <= 22444.5)\n Predict: 0.20842850281013897\n Else (feature 1 > 22444.5)\n Predict: 0.3407345567868832\n Else (feature 7 > 291857.0)\n If (feature 4 <= 122009.5)\n If (feature 1 <= 3609.0)\n Predict: -0.48072036505788196\n Else (feature 1 > 3609.0)\n Predict: 0.2686289913312858\n Else (feature 4 > 122009.5)\n If (feature 3 <= 118308.0)\n Predict: 0.5684571987124794\n Else (feature 3 > 118308.0)\n Predict: -2.5833364070395457\n Else (feature 0 > 3850.0)\n If (feature 0 <= 4678.0)\n If (feature 6 <= 90233.0)\n Predict: -0.5173524110374963\n Else (feature 6 > 90233.0)\n If (feature 0 <= 4674.5)\n Predict: 0.3663878728230446\n Else (feature 0 > 4674.5)\n Predict: 0.4147436850856553\n Else (feature 0 > 4678.0)\n If (feature 4 <= 118916.0)\n If (feature 7 <= 68840.0)\n Predict: 0.5157871943319994\n Else (feature 7 > 68840.0)\n Predict: 0.38366989015141606\n Else (feature 4 > 118916.0)\n If (feature 4 <= 119005.5)\n Predict: -0.7076265347709042\n Else (feature 4 > 119005.5)\n Predict: 0.2604357573097203\n Else (feature 0 > 6970.5)\n If (feature 1 <= 2408.0)\n If (feature 3 <= 117938.0)\n If (feature 3 <= 117928.5)\n If (feature 7 <= 118009.0)\n Predict: -0.9880465467807676\n Else (feature 7 > 118009.0)\n Predict: 0.4378029397715231\n Else (feature 3 > 117928.5)\n If (feature 6 <= 117925.0)\n Predict: 0.40931734198094594\n Else (feature 6 > 117925.0)\n Predict: -3.1001908165370997\n Else (feature 3 > 117938.0)\n If (feature 7 <= 119139.5)\n If (feature 5 <= 117882.0)\n Predict: -0.20790028063067192\n Else (feature 5 > 117882.0)\n Predict: 0.3794431108011829\n Else (feature 7 > 119139.5)\n If (feature 4 <= 6414.5)\n Predict: -0.14516329555153096\n Else (feature 4 > 6414.5)\n Predict: 0.29441700056882575\n Else (feature 1 > 2408.0)\n If (feature 2 <= 117906.0)\n If (feature 4 <= 118208.0)\n If (feature 8 <= 123679.0)\n Predict: 0.28502015484872345\n Else (feature 8 > 123679.0)\n Predict: -0.209779846811499\n Else (feature 4 > 118208.0)\n If (feature 7 <= 19757.0)\n Predict: 0.029122160381951274\n Else (feature 7 > 19757.0)\n Predict: 0.42141668185666525\n Else (feature 2 > 117906.0)\n If (feature 7 <= 68840.0)\n If (feature 1 <= 3081.0)\n Predict: -0.959676344289425\n Else (feature 1 > 3081.0)\n Predict: 0.15014715407277215\n Else (feature 7 > 68840.0)\n If (feature 7 <= 280703.5)\n Predict: 0.2604786375880997\n Else (feature 7 > 280703.5)\n Predict: 0.19492388614561018\n Tree 6 (weight 0.1):\n If (feature 1 <= 13785.5)\n If (feature 2 <= 118763.0)\n If (feature 3 <= 118333.5)\n If (feature 1 <= 7423.0)\n If (feature 1 <= 1590.0)\n Predict: 0.3217996681909174\n Else (feature 1 > 1590.0)\n Predict: 0.19232074288994255\n Else (feature 1 > 7423.0)\n If (feature 7 <= 291857.0)\n Predict: 0.3414604882204112\n Else (feature 7 > 291857.0)\n Predict: 0.09371447418867782\n Else (feature 3 > 118333.5)\n If (feature 4 <= 17949.0)\n If (feature 0 <= 79106.5)\n Predict: 0.17865382471601024\n Else (feature 0 > 79106.5)\n Predict: -1.3272337333570035\n Else (feature 4 > 17949.0)\n If (feature 6 <= 311461.5)\n Predict: 0.3094114855698163\n Else (feature 6 > 311461.5)\n Predict: -0.024401008633738175\n Else (feature 2 > 118763.0)\n If (feature 4 <= 150387.0)\n If (feature 5 <= 118183.0)\n If (feature 0 <= 29292.5)\n Predict: 0.3333274046102349\n Else (feature 0 > 29292.5)\n Predict: -0.3375527180568485\n Else (feature 5 > 118183.0)\n If (feature 6 <= 223721.0)\n Predict: 0.3332502557737523\n Else (feature 6 > 223721.0)\n Predict: 0.057548400071056445\n Else (feature 4 > 150387.0)\n If (feature 0 <= 23981.5)\n If (feature 7 <= 68840.0)\n Predict: -1.3621201693795506\n Else (feature 7 > 68840.0)\n Predict: 0.13407811868259362\n Else (feature 0 > 23981.5)\n If (feature 1 <= 86.5)\n Predict: -0.24221259295521091\n Else (feature 1 > 86.5)\n Predict: -1.4664711943644093\n Else (feature 1 > 13785.5)\n If (feature 1 <= 74189.5)\n If (feature 8 <= 119914.5)\n If (feature 1 <= 71211.5)\n If (feature 8 <= 118971.5)\n Predict: 0.18585788385944002\n Else (feature 8 > 118971.5)\n Predict: 0.044848601036691686\n Else (feature 1 > 71211.5)\n If (feature 4 <= 118208.0)\n Predict: 0.41177156690949795\n Else (feature 4 > 118208.0)\n Predict: -0.45409275740131966\n Else (feature 8 > 119914.5)\n If (feature 6 <= 118970.0)\n If (feature 8 <= 120782.5)\n Predict: -0.8313149473416813\n Else (feature 8 > 120782.5)\n Predict: 0.21654692404986378\n Else (feature 6 > 118970.0)\n If (feature 7 <= 291857.0)\n Predict: 0.2998256836133264\n Else (feature 7 > 291857.0)\n Predict: -0.24488571054272315\n Else (feature 1 > 74189.5)\n If (feature 4 <= 117943.0)\n If (feature 1 <= 111810.5)\n If (feature 4 <= 117908.0)\n Predict: 0.3851886068363911\n Else (feature 4 > 117908.0)\n Predict: 0.08746467156098123\n Else (feature 1 > 111810.5)\n If (feature 3 <= 118078.0)\n Predict: 0.45174829307710185\n Else (feature 3 > 118078.0)\n Predict: -0.39992492332873286\n Else (feature 4 > 117943.0)\n If (feature 5 <= 117882.0)\n If (feature 1 <= 96501.5)\n Predict: 0.4942658073835808\n Else (feature 1 > 96501.5)\n Predict: -1.9801939461726235\n Else (feature 5 > 117882.0)\n If (feature 8 <= 132683.5)\n Predict: 0.33228333453593634\n Else (feature 8 > 132683.5)\n Predict: -0.012283805132195624\n Tree 7 (weight 0.1):\n If (feature 0 <= 6715.5)\n If (feature 0 <= 3850.0)\n If (feature 7 <= 291857.0)\n If (feature 1 <= 4078.5)\n If (feature 6 <= 311385.0)\n Predict: 0.3134677312037823\n Else (feature 6 > 311385.0)\n Predict: -0.41901360817536215\n Else (feature 1 > 4078.5)\n If (feature 1 <= 22444.5)\n Predict: 0.12983353867898537\n Else (feature 1 > 22444.5)\n Predict: 0.2987704818411478\n Else (feature 7 > 291857.0)\n If (feature 4 <= 122009.5)\n If (feature 1 <= 3609.0)\n Predict: -0.4498847905939799\n Else (feature 1 > 3609.0)\n Predict: 0.23296770934392946\n Else (feature 4 > 122009.5)\n If (feature 3 <= 118308.0)\n Predict: 0.4992479172644749\n Else (feature 3 > 118308.0)\n Predict: -2.1302620468348166\n Else (feature 0 > 3850.0)\n If (feature 0 <= 4678.0)\n If (feature 6 <= 90233.0)\n Predict: -0.4762556880123809\n Else (feature 6 > 90233.0)\n If (feature 0 <= 4674.5)\n Predict: 0.3214154141060503\n Else (feature 0 > 4674.5)\n Predict: 0.3674779516209595\n Else (feature 0 > 4678.0)\n If (feature 0 <= 4679.5)\n If (feature 1 <= 1021.0)\n Predict: 0.4159703161799195\n Else (feature 1 > 1021.0)\n Predict: -2.517445969123994\n Else (feature 0 > 4679.5)\n If (feature 6 <= 117909.5)\n Predict: 0.1656971796457971\n Else (feature 6 > 117909.5)\n Predict: 0.32674698864279267\n Else (feature 0 > 6715.5)\n If (feature 6 <= 125105.5)\n If (feature 6 <= 124593.5)\n If (feature 5 <= 117926.0)\n If (feature 5 <= 117882.0)\n Predict: 0.08568785140005432\n Else (feature 5 > 117882.0)\n Predict: 0.2827597383245785\n Else (feature 5 > 117926.0)\n If (feature 5 <= 118035.5)\n Predict: -0.2726411034614314\n Else (feature 5 > 118035.5)\n Predict: 0.15737746755394746\n Else (feature 6 > 124593.5)\n If (feature 4 <= 120314.5)\n If (feature 3 <= 117928.5)\n Predict: -3.5787436462725513\n Else (feature 3 > 117928.5)\n Predict: 0.34125941946545335\n Else (feature 4 > 120314.5)\n If (feature 4 <= 120422.5)\n Predict: -2.9947831290524918\n Else (feature 4 > 120422.5)\n Predict: 0.3915902724116174\n Else (feature 6 > 125105.5)\n If (feature 6 <= 191150.5)\n If (feature 7 <= 118250.0)\n If (feature 4 <= 118516.0)\n Predict: 0.2766833668058247\n Else (feature 4 > 118516.0)\n Predict: 0.025680851592319004\n Else (feature 7 > 118250.0)\n If (feature 4 <= 118175.0)\n Predict: 0.16449086786897066\n Else (feature 4 > 118175.0)\n Predict: 0.30625466012052444\n Else (feature 6 > 191150.5)\n If (feature 2 <= 118237.5)\n If (feature 4 <= 119025.0)\n Predict: 0.1725003129107787\n Else (feature 4 > 119025.0)\n Predict: 0.25770917887511485\n Else (feature 2 > 118237.5)\n If (feature 2 <= 118302.5)\n Predict: -0.2186202679246936\n Else (feature 2 > 118302.5)\n Predict: 0.14778033705761387\n Tree 8 (weight 0.1):\n If (feature 1 <= 13785.5)\n If (feature 2 <= 118763.0)\n If (feature 3 <= 118333.5)\n If (feature 2 <= 118279.5)\n If (feature 8 <= 118805.0)\n Predict: 0.18533116798025223\n Else (feature 8 > 118805.0)\n Predict: 0.26834936077475635\n Else (feature 2 > 118279.5)\n If (feature 4 <= 120240.5)\n Predict: 0.16586754284930486\n Else (feature 4 > 120240.5)\n Predict: -0.4162974733454248\n Else (feature 3 > 118333.5)\n If (feature 4 <= 17949.0)\n If (feature 0 <= 79106.5)\n Predict: 0.1474922169475345\n Else (feature 0 > 79106.5)\n Predict: -1.1864969589171606\n Else (feature 4 > 17949.0)\n If (feature 6 <= 311461.5)\n Predict: 0.2719458571170971\n Else (feature 6 > 311461.5)\n Predict: -0.04204101857069593\n Else (feature 2 > 118763.0)\n If (feature 2 <= 120873.5)\n If (feature 3 <= 120874.5)\n If (feature 4 <= 150387.0)\n Predict: 0.13796697388150345\n Else (feature 4 > 150387.0)\n Predict: -0.609887943698144\n Else (feature 3 > 120874.5)\n If (feature 5 <= 118409.0)\n Predict: -1.7096175968536873\n Else (feature 5 > 118409.0)\n Predict: -0.012812890161053843\n Else (feature 2 > 120873.5)\n If (feature 5 <= 117905.5)\n If (feature 4 <= 120376.5)\n Predict: 1.0879457593642936\n Else (feature 4 > 120376.5)\n Predict: 0.41675802107075377\n Else (feature 5 > 117905.5)\n If (feature 5 <= 118045.0)\n Predict: -0.07635255894004037\n Else (feature 5 > 118045.0)\n Predict: 0.38400150442005593\n Else (feature 1 > 13785.5)\n If (feature 1 <= 74189.5)\n If (feature 2 <= 117927.5)\n If (feature 4 <= 118348.0)\n If (feature 4 <= 118324.0)\n Predict: 0.21506488585739122\n Else (feature 4 > 118324.0)\n Predict: -1.130444358148734\n Else (feature 4 > 118348.0)\n If (feature 7 <= 19757.0)\n Predict: -0.15483059685309172\n Else (feature 7 > 19757.0)\n Predict: 0.3762329225869383\n Else (feature 2 > 117927.5)\n If (feature 1 <= 71211.5)\n If (feature 3 <= 118082.5)\n Predict: 0.061268397240012\n Else (feature 3 > 118082.5)\n Predict: 0.17292035760677937\n Else (feature 1 > 71211.5)\n If (feature 0 <= 41781.5)\n Predict: -0.5797498022962589\n Else (feature 0 > 41781.5)\n Predict: 0.19446324631340584\n Else (feature 1 > 74189.5)\n If (feature 4 <= 117943.0)\n If (feature 3 <= 118025.0)\n If (feature 3 <= 117931.5)\n Predict: 0.22177158410316902\n Else (feature 3 > 117931.5)\n Predict: 0.4246787315081853\n Else (feature 3 > 118025.0)\n If (feature 6 <= 118100.0)\n Predict: 0.2698684753241032\n Else (feature 6 > 118100.0)\n Predict: -0.18493893614773146\n Else (feature 4 > 117943.0)\n If (feature 6 <= 234793.0)\n If (feature 8 <= 132683.5)\n Predict: 0.2697534358771549\n Else (feature 8 > 132683.5)\n Predict: -0.14272335002132983\n Else (feature 6 > 234793.0)\n If (feature 6 <= 311461.5)\n Predict: 0.3535663051561948\n Else (feature 6 > 311461.5)\n Predict: 0.504653297413937\n Tree 9 (weight 0.1):\n If (feature 0 <= 6715.5)\n If (feature 0 <= 3850.0)\n If (feature 3 <= 118046.5)\n If (feature 7 <= 19757.0)\n If (feature 1 <= 58465.5)\n Predict: 0.25483038285596143\n Else (feature 1 > 58465.5)\n Predict: -1.1597418964097053\n Else (feature 7 > 19757.0)\n If (feature 0 <= 680.5)\n Predict: 0.20822267344733938\n Else (feature 0 > 680.5)\n Predict: 0.3692722024367684\n Else (feature 3 > 118046.5)\n If (feature 3 <= 118174.0)\n If (feature 1 <= 17220.5)\n Predict: -0.3837848269324116\n Else (feature 1 > 17220.5)\n Predict: 0.26460021308997356\n Else (feature 3 > 118174.0)\n If (feature 5 <= 118753.5)\n Predict: 0.2676158357096411\n Else (feature 5 > 118753.5)\n Predict: 0.13686395147857738\n Else (feature 0 > 3850.0)\n If (feature 0 <= 4678.0)\n If (feature 0 <= 4674.5)\n If (feature 3 <= 119703.5)\n Predict: 0.29589477165703354\n Else (feature 3 > 119703.5)\n Predict: -0.07194385748738544\n Else (feature 0 > 4674.5)\n If (feature 5 <= 182600.5)\n Predict: 0.3342363549138954\n Else (feature 5 > 182600.5)\n Predict: -0.7244171041772537\n Else (feature 0 > 4678.0)\n If (feature 6 <= 117909.5)\n If (feature 4 <= 143035.5)\n Predict: 0.15034111073243206\n Else (feature 4 > 143035.5)\n Predict: -1.5167389726763751\n Else (feature 6 > 117909.5)\n If (feature 8 <= 124906.0)\n Predict: 0.31055427215851344\n Else (feature 8 > 124906.0)\n Predict: -0.0547177173924219\n Else (feature 0 > 6715.5)\n If (feature 0 <= 25429.0)\n If (feature 0 <= 18069.0)\n If (feature 6 <= 117909.5)\n If (feature 3 <= 118118.0)\n Predict: -0.17951866832199687\n Else (feature 3 > 118118.0)\n Predict: 0.21023269393947489\n Else (feature 6 > 117909.5)\n If (feature 3 <= 31573.5)\n Predict: -1.3213505685067666\n Else (feature 3 > 31573.5)\n Predict: 0.23190883123637848\n Else (feature 0 > 18069.0)\n If (feature 7 <= 291857.0)\n If (feature 4 <= 118296.5)\n Predict: 0.2145772989141054\n Else (feature 4 > 118296.5)\n Predict: 0.048044939758556476\n Else (feature 7 > 291857.0)\n If (feature 4 <= 119650.5)\n Predict: -0.4355424404437331\n Else (feature 4 > 119650.5)\n Predict: 0.19884242292376147\n Else (feature 0 > 25429.0)\n If (feature 6 <= 125105.5)\n If (feature 6 <= 124593.5)\n If (feature 8 <= 118307.5)\n Predict: 0.19960299225357794\n Else (feature 8 > 118307.5)\n Predict: 0.12500028302948274\n Else (feature 6 > 124593.5)\n If (feature 4 <= 120314.5)\n Predict: 0.28403939113851423\n Else (feature 4 > 120314.5)\n Predict: -1.5617417519872143\n Else (feature 6 > 125105.5)\n If (feature 5 <= 118409.0)\n If (feature 3 <= 118581.5)\n Predict: 0.17205656812060446\n Else (feature 3 > 118581.5)\n Predict: -0.13009189434290622\n Else (feature 5 > 118409.0)\n If (feature 7 <= 68840.0)\n Predict: 0.13791414933477536\n Else (feature 7 > 68840.0)\n Predict: 0.24110254313618765\n Tree 10 (weight 0.1):\n If (feature 1 <= 13872.0)\n If (feature 5 <= 289174.0)\n If (feature 2 <= 118117.0)\n If (feature 7 <= 252006.5)\n If (feature 7 <= 5699.0)\n Predict: 0.051112854038403245\n Else (feature 7 > 5699.0)\n Predict: 0.24861529683409073\n Else (feature 7 > 252006.5)\n If (feature 5 <= 118285.5)\n Predict: 0.23085560812498462\n Else (feature 5 > 118285.5)\n Predict: 0.15127085572491505\n Else (feature 2 > 118117.0)\n If (feature 6 <= 194285.0)\n If (feature 4 <= 118981.5)\n Predict: 0.27507275511563367\n Else (feature 4 > 118981.5)\n Predict: 0.06018127587292681\n Else (feature 6 > 194285.0)\n If (feature 6 <= 279306.0)\n Predict: -0.2519772275666675\n Else (feature 6 > 279306.0)\n Predict: 0.17414195331652116\n Else (feature 5 > 289174.0)\n If (feature 1 <= 13837.0)\n If (feature 0 <= 925.5)\n Predict: -3.3284262539637752\n Else (feature 0 > 925.5)\n If (feature 4 <= 118442.0)\n Predict: -0.42049845606410424\n Else (feature 4 > 118442.0)\n Predict: 0.11461571384052587\n Else (feature 1 > 13837.0)\n If (feature 0 <= 28221.5)\n If (feature 6 <= 306540.0)\n Predict: -3.619423097526475\n Else (feature 6 > 306540.0)\n Predict: -3.3670738093203028\n Else (feature 0 > 28221.5)\n If (feature 6 <= 310609.0)\n Predict: -0.028006012235717703\n Else (feature 6 > 310609.0)\n Predict: -3.377358712985492\n Else (feature 1 > 13872.0)\n If (feature 1 <= 74189.5)\n If (feature 1 <= 14723.5)\n If (feature 8 <= 118696.0)\n If (feature 6 <= 281762.5)\n Predict: 0.09667360266039488\n Else (feature 6 > 281762.5)\n Predict: -1.0389413660329259\n Else (feature 8 > 118696.0)\n If (feature 5 <= 118852.0)\n Predict: -1.8727394434336082\n Else (feature 5 > 118852.0)\n Predict: 0.4462901268564155\n Else (feature 1 > 14723.5)\n If (feature 2 <= 120030.0)\n If (feature 3 <= 119762.5)\n Predict: 0.1313723645632764\n Else (feature 3 > 119762.5)\n Predict: -0.3165432693663621\n Else (feature 2 > 120030.0)\n If (feature 5 <= 117905.5)\n Predict: 0.6523363238037354\n Else (feature 5 > 117905.5)\n Predict: 0.3182166980200028\n Else (feature 1 > 74189.5)\n If (feature 4 <= 117943.0)\n If (feature 3 <= 118346.5)\n If (feature 1 <= 111810.5)\n Predict: 0.2580263749225583\n Else (feature 1 > 111810.5)\n Predict: -0.038761355190200804\n Else (feature 3 > 118346.5)\n If (feature 4 <= 117899.5)\n Predict: 0.29805502461906475\n Else (feature 4 > 117899.5)\n Predict: -1.4073324341059754\n Else (feature 4 > 117943.0)\n If (feature 5 <= 117882.0)\n If (feature 0 <= 38391.5)\n Predict: -2.586858848500139\n Else (feature 0 > 38391.5)\n Predict: -0.011774049733914568\n Else (feature 5 > 117882.0)\n If (feature 8 <= 132683.5)\n Predict: 0.2634846478271706\n Else (feature 8 > 132683.5)\n Predict: -0.04220697277442831\n Tree 11 (weight 0.1):\n If (feature 0 <= 6715.5)\n If (feature 0 <= 3850.0)\n If (feature 7 <= 291857.0)\n If (feature 1 <= 4078.5)\n If (feature 6 <= 311385.0)\n Predict: 0.2518740600878059\n Else (feature 6 > 311385.0)\n Predict: -0.4472395825638884\n Else (feature 1 > 4078.5)\n If (feature 1 <= 22444.5)\n Predict: 0.08167559295602035\n Else (feature 1 > 22444.5)\n Predict: 0.24095806442662146\n Else (feature 7 > 291857.0)\n If (feature 6 <= 170169.0)\n If (feature 0 <= 1021.0)\n Predict: 0.012097648437168712\n Else (feature 0 > 1021.0)\n Predict: 0.5665871504967913\n Else (feature 6 > 170169.0)\n If (feature 1 <= 3609.0)\n Predict: -1.8056389313395573\n Else (feature 1 > 3609.0)\n Predict: -0.03227205836728203\n Else (feature 0 > 3850.0)\n If (feature 0 <= 4678.0)\n If (feature 6 <= 90233.0)\n Predict: -0.47728705624861795\n Else (feature 6 > 90233.0)\n If (feature 0 <= 4674.5)\n Predict: 0.25886244073981857\n Else (feature 0 > 4674.5)\n Predict: 0.30079522720556257\n Else (feature 0 > 4678.0)\n If (feature 6 <= 117909.5)\n If (feature 1 <= 52021.5)\n Predict: 0.1566897902180596\n Else (feature 1 > 52021.5)\n Predict: -0.6107550055436679\n Else (feature 6 > 117909.5)\n If (feature 8 <= 124906.0)\n Predict: 0.2799460694890645\n Else (feature 8 > 124906.0)\n Predict: -0.05993847497846616\n Else (feature 0 > 6715.5)\n If (feature 0 <= 23532.5)\n If (feature 0 <= 20889.0)\n If (feature 2 <= 118237.5)\n If (feature 3 <= 118131.5)\n Predict: 0.10321084890072924\n Else (feature 3 > 118131.5)\n Predict: 0.19555003066545076\n Else (feature 2 > 118237.5)\n If (feature 0 <= 18069.0)\n Predict: 0.11554825801948053\n Else (feature 0 > 18069.0)\n Predict: -0.29944151586265433\n Else (feature 0 > 20889.0)\n If (feature 0 <= 21393.0)\n If (feature 6 <= 300090.0)\n Predict: -0.7860210792486457\n Else (feature 6 > 300090.0)\n Predict: -2.6224223601417047\n Else (feature 0 > 21393.0)\n If (feature 6 <= 117909.5)\n Predict: -0.19187399473319774\n Else (feature 6 > 117909.5)\n Predict: 0.15312383612163213\n Else (feature 0 > 23532.5)\n If (feature 6 <= 128574.5)\n If (feature 5 <= 117926.0)\n If (feature 5 <= 117882.0)\n Predict: 0.03610799051849286\n Else (feature 5 > 117882.0)\n Predict: 0.22795916880215175\n Else (feature 5 > 117926.0)\n If (feature 5 <= 118045.0)\n Predict: -0.25422434725765786\n Else (feature 5 > 118045.0)\n Predict: 0.12509996789287578\n Else (feature 6 > 128574.5)\n If (feature 6 <= 191150.5)\n If (feature 7 <= 118168.0)\n Predict: 0.12965000733886742\n Else (feature 7 > 118168.0)\n Predict: 0.2541132542585851\n Else (feature 6 > 191150.5)\n If (feature 4 <= 119005.5)\n Predict: 0.10413686030393719\n Else (feature 4 > 119005.5)\n Predict: 0.20398191596571572\n Tree 12 (weight 0.1):\n If (feature 1 <= 13872.0)\n If (feature 5 <= 289174.0)\n If (feature 2 <= 118920.0)\n If (feature 3 <= 118333.5)\n If (feature 1 <= 7395.5)\n Predict: 0.1397623828105277\n Else (feature 1 > 7395.5)\n Predict: 0.234048494830281\n Else (feature 3 > 118333.5)\n If (feature 4 <= 118633.0)\n Predict: 0.14431751471094623\n Else (feature 4 > 118633.0)\n Predict: 0.23291577853650391\n Else (feature 2 > 118920.0)\n If (feature 2 <= 120873.5)\n If (feature 3 <= 120349.0)\n Predict: 0.07549383904293043\n Else (feature 3 > 120349.0)\n Predict: -0.5398598437283596\n Else (feature 2 > 120873.5)\n If (feature 5 <= 117905.5)\n Predict: 0.7511792951373342\n Else (feature 5 > 117905.5)\n Predict: 0.25174584991288074\n Else (feature 5 > 289174.0)\n If (feature 1 <= 13837.0)\n If (feature 6 <= 306540.0)\n If (feature 1 <= 12262.5)\n Predict: 0.13569874960097514\n Else (feature 1 > 12262.5)\n Predict: -1.6915684157438395\n Else (feature 6 > 306540.0)\n If (feature 0 <= 79030.5)\n Predict: -0.04763880170615885\n Else (feature 0 > 79030.5)\n Predict: -1.533621776040018\n Else (feature 1 > 13837.0)\n If (feature 0 <= 28221.5)\n If (feature 4 <= 118318.5)\n Predict: -2.93903156557859\n Else (feature 4 > 118318.5)\n Predict: -3.30472072321972\n Else (feature 0 > 28221.5)\n If (feature 6 <= 310609.0)\n Predict: -0.034065336347180133\n Else (feature 6 > 310609.0)\n Predict: -2.9524910315116184\n Else (feature 1 > 13872.0)\n If (feature 1 <= 77514.0)\n If (feature 2 <= 117927.5)\n If (feature 6 <= 117891.5)\n If (feature 2 <= 117917.0)\n Predict: -0.08556494203846698\n Else (feature 2 > 117917.0)\n Predict: 0.5107196020022438\n Else (feature 6 > 117891.5)\n If (feature 8 <= 128292.0)\n Predict: 0.23802938442359703\n Else (feature 8 > 128292.0)\n Predict: -0.1391617454018253\n Else (feature 2 > 117927.5)\n If (feature 6 <= 292091.5)\n If (feature 6 <= 280461.0)\n Predict: 0.0961080975070299\n Else (feature 6 > 280461.0)\n Predict: -0.3106877174465286\n Else (feature 6 > 292091.5)\n If (feature 1 <= 15405.0)\n Predict: -0.4027741524948062\n Else (feature 1 > 15405.0)\n Predict: 0.23416600545811275\n Else (feature 1 > 77514.0)\n If (feature 4 <= 117943.0)\n If (feature 3 <= 118025.0)\n If (feature 3 <= 117931.5)\n Predict: 0.1733366889553659\n Else (feature 3 > 117931.5)\n Predict: 0.36336847948961215\n Else (feature 3 > 118025.0)\n If (feature 6 <= 118100.0)\n Predict: 0.2120753369082636\n Else (feature 6 > 118100.0)\n Predict: -0.23869928860869818\n Else (feature 4 > 117943.0)\n If (feature 5 <= 117882.0)\n If (feature 1 <= 96501.5)\n Predict: 0.4085083253547172\n Else (feature 1 > 96501.5)\n Predict: -1.4831462016921624\n Else (feature 5 > 117882.0)\n If (feature 6 <= 234793.0)\n Predict: 0.2212464986894127\n Else (feature 6 > 234793.0)\n Predict: 0.3021667577971601\n Tree 13 (weight 0.1):\n If (feature 7 <= 68840.0)\n If (feature 7 <= 19757.0)\n If (feature 3 <= 118005.5)\n If (feature 0 <= 290221.0)\n If (feature 4 <= 117881.0)\n Predict: 0.30658469343038136\n Else (feature 4 > 117881.0)\n Predict: 0.10287505061512729\n Else (feature 0 > 290221.0)\n Predict: -3.6179673361876326\n Else (feature 3 > 118005.5)\n If (feature 3 <= 118082.5)\n If (feature 0 <= 38705.0)\n Predict: -0.6271992529957928\n Else (feature 0 > 38705.0)\n Predict: 0.06793266773050918\n Else (feature 3 > 118082.5)\n If (feature 1 <= 274318.5)\n Predict: 0.11318803134014759\n Else (feature 1 > 274318.5)\n Predict: -2.5896241996118534\n Else (feature 7 > 19757.0)\n If (feature 5 <= 118679.5)\n If (feature 6 <= 131852.5)\n If (feature 1 <= 18045.0)\n Predict: -0.9711888715041896\n Else (feature 1 > 18045.0)\n Predict: -2.822458620913035\n Else (feature 6 > 131852.5)\n If (feature 0 <= 18069.0)\n Predict: 0.4331307268830786\n Else (feature 0 > 18069.0)\n Predict: -0.8600045882988321\n Else (feature 5 > 118679.5)\n If (feature 3 <= 118591.5)\n If (feature 0 <= 42097.5)\n Predict: -0.04860395394323738\n Else (feature 0 > 42097.5)\n Predict: 0.4181028063729765\n Else (feature 3 > 118591.5)\n If (feature 2 <= 118920.0)\n Predict: -2.492511242689689\n Else (feature 2 > 118920.0)\n Predict: 0.15491204744294151\n Else (feature 7 > 68840.0)\n If (feature 7 <= 262441.5)\n If (feature 6 <= 163977.5)\n If (feature 4 <= 117908.0)\n If (feature 1 <= 111810.5)\n Predict: 0.28584760226449474\n Else (feature 1 > 111810.5)\n Predict: 0.013586674282938324\n Else (feature 4 > 117908.0)\n If (feature 4 <= 118393.0)\n Predict: 0.09481122501893016\n Else (feature 4 > 118393.0)\n Predict: 0.19495267490313886\n Else (feature 6 > 163977.5)\n If (feature 8 <= 118412.0)\n If (feature 6 <= 269410.5)\n Predict: -0.17029488487055267\n Else (feature 6 > 269410.5)\n Predict: 0.1668416214581979\n Else (feature 8 > 118412.0)\n If (feature 7 <= 118168.0)\n Predict: -0.12161713759360851\n Else (feature 7 > 118168.0)\n Predict: 0.17686482876976642\n Else (feature 7 > 262441.5)\n If (feature 5 <= 117905.5)\n If (feature 3 <= 118346.5)\n If (feature 4 <= 118348.0)\n Predict: 0.07897534358932572\n Else (feature 4 > 118348.0)\n Predict: 0.2407564265489645\n Else (feature 3 > 118346.5)\n If (feature 1 <= 60164.0)\n Predict: 0.15735459494972143\n Else (feature 1 > 60164.0)\n Predict: -0.1709662743300348\n Else (feature 5 > 117905.5)\n If (feature 6 <= 125786.0)\n If (feature 1 <= 15648.5)\n Predict: 0.11081771357913414\n Else (feature 1 > 15648.5)\n Predict: -0.03202425816910368\n Else (feature 6 > 125786.0)\n If (feature 6 <= 183984.0)\n Predict: 0.2415642753501299\n Else (feature 6 > 183984.0)\n Predict: 0.10146727894195419\n Tree 14 (weight 0.1):\n If (feature 0 <= 6715.5)\n If (feature 0 <= 3850.0)\n If (feature 8 <= 118047.5)\n If (feature 1 <= 111810.5)\n If (feature 2 <= 120030.0)\n Predict: 0.2718562213030836\n Else (feature 2 > 120030.0)\n Predict: -0.22574921935567585\n Else (feature 1 > 111810.5)\n If (feature 3 <= 118082.5)\n Predict: -3.3913680509215816\n Else (feature 3 > 118082.5)\n Predict: 0.2539246493004062\n Else (feature 8 > 118047.5)\n If (feature 4 <= 122162.0)\n If (feature 8 <= 119156.5)\n Predict: 0.09952516181986319\n Else (feature 8 > 119156.5)\n Predict: 0.23089277907960992\n Else (feature 4 > 122162.0)\n If (feature 7 <= 300684.5)\n Predict: 0.04368823085375718\n Else (feature 7 > 300684.5)\n Predict: -1.3181660402110902\n Else (feature 0 > 3850.0)\n If (feature 0 <= 4678.0)\n If (feature 0 <= 4674.5)\n If (feature 3 <= 119703.5)\n Predict: 0.23626106404219022\n Else (feature 3 > 119703.5)\n Predict: -0.0957878162988562\n Else (feature 0 > 4674.5)\n If (feature 5 <= 182600.5)\n Predict: 0.272347607787026\n Else (feature 5 > 182600.5)\n Predict: -0.7209113090950439\n Else (feature 0 > 4678.0)\n If (feature 6 <= 117909.5)\n If (feature 4 <= 143035.5)\n Predict: 0.10568829441555601\n Else (feature 4 > 143035.5)\n Predict: -1.4161285377721793\n Else (feature 6 > 117909.5)\n If (feature 8 <= 124906.0)\n Predict: 0.24695548203394307\n Else (feature 8 > 124906.0)\n Predict: -0.07431876478548292\n Else (feature 0 > 6715.5)\n If (feature 0 <= 73588.0)\n If (feature 7 <= 68840.0)\n If (feature 4 <= 118525.5)\n If (feature 2 <= 117976.5)\n Predict: 0.1771101538789854\n Else (feature 2 > 117976.5)\n Predict: -0.020820640532325435\n Else (feature 4 > 118525.5)\n If (feature 5 <= 118679.5)\n Predict: -1.1852207553761964\n Else (feature 5 > 118679.5)\n Predict: -0.06868578323347717\n Else (feature 7 > 68840.0)\n If (feature 7 <= 262441.5)\n If (feature 6 <= 118298.0)\n Predict: 0.22982492133468183\n Else (feature 6 > 118298.0)\n Predict: 0.13534182864018063\n Else (feature 7 > 262441.5)\n If (feature 1 <= 17404.0)\n Predict: 0.11538236649648564\n Else (feature 1 > 17404.0)\n Predict: 0.03251733958504101\n Else (feature 0 > 73588.0)\n If (feature 0 <= 79128.5)\n If (feature 4 <= 117916.0)\n If (feature 2 <= 117981.5)\n Predict: 0.2527653101983945\n Else (feature 2 > 117981.5)\n Predict: 0.3623115602982322\n Else (feature 4 > 117916.0)\n If (feature 4 <= 118504.0)\n Predict: 0.12146118885040794\n Else (feature 4 > 118504.0)\n Predict: 0.20278044865944161\n Else (feature 0 > 79128.5)\n If (feature 2 <= 118110.0)\n If (feature 4 <= 17949.0)\n Predict: -0.5773113275378013\n Else (feature 4 > 17949.0)\n Predict: 0.14105777163439082\n Else (feature 2 > 118110.0)\n If (feature 7 <= 3901.5)\n Predict: -3.5873977397599432\n Else (feature 7 > 3901.5)\n Predict: -0.013331198210001526\n Tree 15 (weight 0.1):\n If (feature 0 <= 6715.5)\n If (feature 0 <= 3850.0)\n If (feature 3 <= 118046.5)\n If (feature 7 <= 19757.0)\n If (feature 1 <= 58465.5)\n Predict: 0.1732341712565485\n Else (feature 1 > 58465.5)\n Predict: -1.1106525626397676\n Else (feature 7 > 19757.0)\n If (feature 0 <= 680.5)\n Predict: 0.13802591418559548\n Else (feature 0 > 680.5)\n Predict: 0.2909351108230361\n Else (feature 3 > 118046.5)\n If (feature 3 <= 118174.0)\n If (feature 1 <= 17220.5)\n Predict: -0.4065238630612877\n Else (feature 1 > 17220.5)\n Predict: 0.19726349026604098\n Else (feature 3 > 118174.0)\n If (feature 5 <= 118753.5)\n Predict: 0.20240132854060366\n Else (feature 5 > 118753.5)\n Predict: 0.08224479175817877\n Else (feature 0 > 3850.0)\n If (feature 0 <= 4678.0)\n If (feature 6 <= 90233.0)\n Predict: -0.4737639275021292\n Else (feature 6 > 90233.0)\n If (feature 0 <= 4674.5)\n Predict: 0.215763506460904\n Else (feature 0 > 4674.5)\n Predict: 0.25524471771611523\n Else (feature 0 > 4678.0)\n If (feature 0 <= 4679.5)\n If (feature 1 <= 1021.0)\n Predict: 0.32163582710050426\n Else (feature 1 > 1021.0)\n Predict: -2.0063891708437778\n Else (feature 0 > 4679.5)\n If (feature 4 <= 122248.5)\n Predict: 0.20795040095560124\n Else (feature 4 > 122248.5)\n Predict: 0.026927643503685945\n Else (feature 0 > 6715.5)\n If (feature 6 <= 119472.5)\n If (feature 8 <= 118320.5)\n If (feature 6 <= 118298.0)\n If (feature 7 <= 19757.0)\n Predict: 0.015422325883357851\n Else (feature 7 > 19757.0)\n Predict: 0.1942467346971747\n Else (feature 6 > 118298.0)\n If (feature 3 <= 117907.0)\n Predict: -1.867332928923562\n Else (feature 3 > 117907.0)\n Predict: -0.007131231851537258\n Else (feature 8 > 118320.5)\n If (feature 1 <= 3794.0)\n If (feature 3 <= 119113.0)\n Predict: 0.17711266946608828\n Else (feature 3 > 119113.0)\n Predict: -0.7607353903038785\n Else (feature 1 > 3794.0)\n If (feature 3 <= 118088.0)\n Predict: -0.17086101958522587\n Else (feature 3 > 118088.0)\n Predict: 0.02683918995154796\n Else (feature 6 > 119472.5)\n If (feature 2 <= 117917.0)\n If (feature 8 <= 128292.0)\n If (feature 6 <= 172497.5)\n Predict: 0.27642081648901573\n Else (feature 6 > 172497.5)\n Predict: 0.18249642019543888\n Else (feature 8 > 128292.0)\n If (feature 1 <= 46111.0)\n Predict: 0.32339761301085623\n Else (feature 1 > 46111.0)\n Predict: -0.22846423957193165\n Else (feature 2 > 117917.0)\n If (feature 0 <= 23532.5)\n If (feature 0 <= 18069.0)\n Predict: 0.15773588554378076\n Else (feature 0 > 18069.0)\n Predict: -0.05483389885312693\n Else (feature 0 > 23532.5)\n If (feature 5 <= 118409.0)\n Predict: 0.07961225769340914\n Else (feature 5 > 118409.0)\n Predict: 0.1524229157791208\n Tree 16 (weight 0.1):\n If (feature 1 <= 1590.0)\n If (feature 1 <= 86.5)\n If (feature 3 <= 117965.5)\n If (feature 6 <= 117909.5)\n If (feature 0 <= 44709.5)\n Predict: -0.4698242354347402\n Else (feature 0 > 44709.5)\n Predict: -2.5133963074612518\n Else (feature 6 > 117909.5)\n If (feature 0 <= 4676.0)\n Predict: 0.24151767664391993\n Else (feature 0 > 4676.0)\n Predict: 0.29146395855181506\n Else (feature 3 > 117965.5)\n If (feature 0 <= 34937.0)\n If (feature 0 <= 34614.0)\n Predict: 0.08954932804907573\n Else (feature 0 > 34614.0)\n Predict: -1.4425712090306326\n Else (feature 0 > 34937.0)\n If (feature 2 <= 117968.0)\n Predict: 0.2840437650996546\n Else (feature 2 > 117968.0)\n Predict: 0.5637223943372192\n Else (feature 1 > 86.5)\n If (feature 4 <= 118864.5)\n If (feature 4 <= 118510.5)\n If (feature 1 <= 244.5)\n Predict: -0.03539479846587783\n Else (feature 1 > 244.5)\n Predict: 0.22702342599811548\n Else (feature 4 > 118510.5)\n If (feature 6 <= 147115.0)\n Predict: 0.1836768892462413\n Else (feature 6 > 147115.0)\n Predict: -0.25104079639351756\n Else (feature 4 > 118864.5)\n If (feature 1 <= 733.5)\n If (feature 6 <= 290805.5)\n Predict: 0.2795656117419217\n Else (feature 6 > 290805.5)\n Predict: 0.8031658941529969\n Else (feature 1 > 733.5)\n If (feature 0 <= 81255.5)\n Predict: 0.21247182749174334\n Else (feature 0 > 81255.5)\n Predict: 0.09659978825542766\n Else (feature 1 > 1590.0)\n If (feature 6 <= 119472.5)\n If (feature 1 <= 1749.5)\n If (feature 0 <= 41440.5)\n If (feature 0 <= 15718.0)\n Predict: 0.28289038216213147\n Else (feature 0 > 15718.0)\n Predict: -3.1634663231518707\n Else (feature 0 > 41440.5)\n If (feature 0 <= 75216.5)\n Predict: 0.3722031970164513\n Else (feature 0 > 75216.5)\n Predict: 0.3221193666225968\n Else (feature 1 > 1749.5)\n If (feature 8 <= 118320.5)\n If (feature 6 <= 119101.0)\n Predict: 0.14307994321879997\n Else (feature 6 > 119101.0)\n Predict: -0.5311632332321474\n Else (feature 8 > 118320.5)\n If (feature 3 <= 118088.0)\n Predict: -0.12204495428410418\n Else (feature 3 > 118088.0)\n Predict: 0.056176499894055855\n Else (feature 6 > 119472.5)\n If (feature 2 <= 118117.0)\n If (feature 8 <= 118820.5)\n If (feature 6 <= 310609.0)\n Predict: 0.11823737825852307\n Else (feature 6 > 310609.0)\n Predict: -0.13152535865435006\n Else (feature 8 > 118820.5)\n If (feature 6 <= 123196.5)\n Predict: 0.2693828747817892\n Else (feature 6 > 123196.5)\n Predict: 0.1527041361017464\n Else (feature 2 > 118117.0)\n If (feature 1 <= 27871.5)\n If (feature 1 <= 26333.0)\n Predict: 0.019331926762951875\n Else (feature 1 > 26333.0)\n Predict: -1.6519667055916964\n Else (feature 1 > 27871.5)\n If (feature 7 <= 291857.0)\n Predict: 0.2146341409297987\n Else (feature 7 > 291857.0)\n Predict: -0.07664767234122873\n Tree 17 (weight 0.1):\n If (feature 0 <= 6715.5)\n If (feature 0 <= 3850.0)\n If (feature 8 <= 118047.5)\n If (feature 1 <= 111810.5)\n If (feature 2 <= 120030.0)\n Predict: 0.24150530545414675\n Else (feature 2 > 120030.0)\n Predict: -0.23425958355812201\n Else (feature 1 > 111810.5)\n If (feature 3 <= 118082.5)\n Predict: -3.002988927585384\n Else (feature 3 > 118082.5)\n Predict: 0.22686686179041557\n Else (feature 8 > 118047.5)\n If (feature 1 <= 4078.5)\n If (feature 7 <= 291857.0)\n Predict: 0.18039895956069898\n Else (feature 7 > 291857.0)\n Predict: -0.36748731050959343\n Else (feature 1 > 4078.5)\n If (feature 7 <= 118470.5)\n Predict: 0.1663393258993804\n Else (feature 7 > 118470.5)\n Predict: -0.005870572447787561\n Else (feature 0 > 3850.0)\n If (feature 0 <= 4684.5)\n If (feature 0 <= 4674.5)\n If (feature 6 <= 141606.0)\n Predict: 0.14075263975357918\n Else (feature 6 > 141606.0)\n Predict: 0.25118122592562375\n Else (feature 0 > 4674.5)\n If (feature 5 <= 182600.5)\n Predict: 0.23996462997189438\n Else (feature 5 > 182600.5)\n Predict: -0.6637894039415452\n Else (feature 0 > 4684.5)\n If (feature 4 <= 122248.5)\n If (feature 8 <= 124906.0)\n Predict: 0.20142810995381694\n Else (feature 8 > 124906.0)\n Predict: -0.17023219255055366\n Else (feature 4 > 122248.5)\n If (feature 6 <= 125786.0)\n Predict: -0.2695056250117091\n Else (feature 6 > 125786.0)\n Predict: 0.18335568715651152\n Else (feature 0 > 6715.5)\n If (feature 1 <= 2408.0)\n If (feature 3 <= 117938.0)\n If (feature 3 <= 117928.5)\n If (feature 7 <= 118009.0)\n Predict: -0.8503984444564671\n Else (feature 7 > 118009.0)\n Predict: 0.270593772279681\n Else (feature 3 > 117928.5)\n If (feature 6 <= 117925.0)\n Predict: 0.25898285466552645\n Else (feature 6 > 117925.0)\n Predict: -3.007663983437899\n Else (feature 3 > 117938.0)\n If (feature 7 <= 119139.5)\n If (feature 5 <= 117882.0)\n Predict: -0.3153209292207083\n Else (feature 5 > 117882.0)\n Predict: 0.2236757347380874\n Else (feature 7 > 119139.5)\n If (feature 0 <= 15714.5)\n Predict: -0.0497713248561656\n Else (feature 0 > 15714.5)\n Predict: 0.1522839922243058\n Else (feature 1 > 2408.0)\n If (feature 1 <= 2906.0)\n If (feature 8 <= 120330.5)\n If (feature 4 <= 123485.0)\n Predict: 0.14240685480847934\n Else (feature 4 > 123485.0)\n Predict: -0.9260237073793252\n Else (feature 8 > 120330.5)\n If (feature 6 <= 302940.0)\n Predict: -0.28744487560763\n Else (feature 6 > 302940.0)\n Predict: -1.8086135666877643\n Else (feature 1 > 2906.0)\n If (feature 6 <= 125105.5)\n If (feature 6 <= 124593.5)\n Predict: 0.07538926149658601\n Else (feature 6 > 124593.5)\n Predict: -0.7045638056241335\n Else (feature 6 > 125105.5)\n If (feature 3 <= 118897.5)\n Predict: 0.13189597142178805\n Else (feature 3 > 118897.5)\n Predict: 0.009727628471585474\n Tree 18 (weight 0.1):\n If (feature 7 <= 68840.0)\n If (feature 3 <= 117957.0)\n If (feature 6 <= 126084.0)\n If (feature 4 <= 117881.0)\n If (feature 0 <= 14462.0)\n Predict: 0.33465251065984675\n Else (feature 0 > 14462.0)\n Predict: 0.17799111977170515\n Else (feature 4 > 117881.0)\n If (feature 1 <= 50808.0)\n Predict: 0.1340238904102969\n Else (feature 1 > 50808.0)\n Predict: -0.19173316703327237\n Else (feature 6 > 126084.0)\n If (feature 6 <= 241900.0)\n If (feature 1 <= 2008.5)\n Predict: -0.2309737421725885\n Else (feature 1 > 2008.5)\n Predict: 0.29275834248311844\n Else (feature 6 > 241900.0)\n If (feature 6 <= 266869.0)\n Predict: -0.49742865966269595\n Else (feature 6 > 266869.0)\n Predict: 0.24036534644405652\n Else (feature 3 > 117957.0)\n If (feature 7 <= 19757.0)\n If (feature 4 <= 120404.0)\n If (feature 6 <= 121579.5)\n Predict: -0.052829785247092274\n Else (feature 6 > 121579.5)\n Predict: 0.14423557057194503\n Else (feature 4 > 120404.0)\n If (feature 4 <= 120664.5)\n Predict: -0.7893134153343626\n Else (feature 4 > 120664.5)\n Predict: 0.04866665945933185\n Else (feature 7 > 19757.0)\n If (feature 5 <= 118679.5)\n If (feature 1 <= 30529.0)\n Predict: -0.6769845658922546\n Else (feature 1 > 30529.0)\n Predict: -2.012339874725547\n Else (feature 5 > 118679.5)\n If (feature 3 <= 118591.5)\n Predict: -0.0040980200693427685\n Else (feature 3 > 118591.5)\n Predict: -0.8045046232334488\n Else (feature 7 > 68840.0)\n If (feature 2 <= 117924.0)\n If (feature 4 <= 118558.0)\n If (feature 4 <= 118038.5)\n If (feature 3 <= 118157.0)\n Predict: 0.24034065336207727\n Else (feature 3 > 118157.0)\n Predict: -0.09205214682946965\n Else (feature 4 > 118038.5)\n If (feature 4 <= 118064.5)\n Predict: -0.7365429727852798\n Else (feature 4 > 118064.5)\n Predict: 0.12205065570752394\n Else (feature 4 > 118558.0)\n If (feature 6 <= 118032.5)\n If (feature 8 <= 118287.5)\n Predict: 0.2442536733129404\n Else (feature 8 > 118287.5)\n Predict: 0.44564533476438944\n Else (feature 6 > 118032.5)\n If (feature 1 <= 1180.0)\n Predict: -0.07500125035644267\n Else (feature 1 > 1180.0)\n Predict: 0.2478314960166956\n Else (feature 2 > 117924.0)\n If (feature 3 <= 118180.0)\n If (feature 4 <= 118974.5)\n If (feature 4 <= 118843.0)\n Predict: 0.08323388792630801\n Else (feature 4 > 118843.0)\n Predict: 0.28021349389285866\n Else (feature 4 > 118974.5)\n If (feature 4 <= 119005.5)\n Predict: -1.619850816718554\n Else (feature 4 > 119005.5)\n Predict: 0.03654781337774564\n Else (feature 3 > 118180.0)\n If (feature 6 <= 250534.5)\n If (feature 6 <= 123871.5)\n Predict: 0.09979957072847474\n Else (feature 6 > 123871.5)\n Predict: 0.15752205500167168\n Else (feature 6 > 250534.5)\n If (feature 6 <= 287248.0)\n Predict: -0.03515322182139593\n Else (feature 6 > 287248.0)\n Predict: 0.135684638238401\n Tree 19 (weight 0.1):\n If (feature 1 <= 13872.0)\n If (feature 5 <= 289174.0)\n If (feature 2 <= 118920.0)\n If (feature 3 <= 118333.5)\n If (feature 1 <= 7395.5)\n Predict: 0.08795468811463171\n Else (feature 1 > 7395.5)\n Predict: 0.1789759547900717\n Else (feature 3 > 118333.5)\n If (feature 1 <= 2906.0)\n Predict: 0.09374501988956899\n Else (feature 1 > 2906.0)\n Predict: 0.1796556326458588\n Else (feature 2 > 118920.0)\n If (feature 5 <= 118183.0)\n If (feature 0 <= 29292.5)\n Predict: 0.18672045795233683\n Else (feature 0 > 29292.5)\n Predict: -0.45006254955823893\n Else (feature 5 > 118183.0)\n If (feature 4 <= 150387.0)\n Predict: 0.11061329838330639\n Else (feature 4 > 150387.0)\n Predict: -0.7714938554039261\n Else (feature 5 > 289174.0)\n If (feature 1 <= 6218.5)\n If (feature 0 <= 925.5)\n Predict: -2.959697234225924\n Else (feature 0 > 925.5)\n If (feature 3 <= 118231.0)\n Predict: -0.40977249804544774\n Else (feature 3 > 118231.0)\n Predict: 0.10080092357349799\n Else (feature 1 > 6218.5)\n If (feature 4 <= 118525.5)\n If (feature 3 <= 118321.5)\n Predict: 0.4505342984929219\n Else (feature 3 > 118321.5)\n Predict: -1.316401604599609\n Else (feature 4 > 118525.5)\n If (feature 6 <= 296063.0)\n Predict: -0.7028699304132203\n Else (feature 6 > 296063.0)\n Predict: 0.5595445933269847\n Else (feature 1 > 13872.0)\n If (feature 1 <= 77514.0)\n If (feature 2 <= 120030.0)\n If (feature 3 <= 119762.5)\n If (feature 1 <= 14868.0)\n Predict: -0.11233745597017039\n Else (feature 1 > 14868.0)\n Predict: 0.06647548075629711\n Else (feature 3 > 119762.5)\n If (feature 1 <= 25345.5)\n Predict: -1.1847275666490604\n Else (feature 1 > 25345.5)\n Predict: 0.2023640781449173\n Else (feature 2 > 120030.0)\n If (feature 1 <= 15405.0)\n If (feature 1 <= 14868.0)\n Predict: 0.3843361712509843\n Else (feature 1 > 14868.0)\n Predict: -1.019168570913126\n Else (feature 1 > 15405.0)\n If (feature 6 <= 117909.5)\n Predict: 0.44703663751040973\n Else (feature 6 > 117909.5)\n Predict: 0.26893800819971386\n Else (feature 1 > 77514.0)\n If (feature 4 <= 117943.0)\n If (feature 3 <= 118025.0)\n If (feature 3 <= 117931.5)\n Predict: 0.12636955190628169\n Else (feature 3 > 117931.5)\n Predict: 0.2962409623075088\n Else (feature 3 > 118025.0)\n If (feature 1 <= 79438.5)\n Predict: -0.9803447783046636\n Else (feature 1 > 79438.5)\n Predict: 0.005810885257123011\n Else (feature 4 > 117943.0)\n If (feature 6 <= 234793.0)\n If (feature 6 <= 223721.0)\n Predict: 0.18220769305442053\n Else (feature 6 > 223721.0)\n Predict: -0.11796975293867717\n Else (feature 6 > 234793.0)\n If (feature 6 <= 311461.5)\n Predict: 0.24137781357742444\n Else (feature 6 > 311461.5)\n Predict: 0.36712455622832807\n'
In [47]:
model.stages[1].save('AmazonGBT')
model_df = spark.read.parquet("AmazonGBT/data")
print(model_df.printSchema())
model_df.toJSON().collect()
root
|-- treeID: integer (nullable = true)
|-- nodeData: struct (nullable = true)
| |-- id: integer (nullable = true)
| |-- prediction: double (nullable = true)
| |-- impurity: double (nullable = true)
| |-- impurityStats: array (nullable = true)
| | |-- element: double (containsNull = true)
| |-- gain: double (nullable = true)
| |-- leftChild: integer (nullable = true)
| |-- rightChild: integer (nullable = true)
| |-- split: struct (nullable = true)
| | |-- featureIndex: integer (nullable = true)
| | |-- leftCategoriesOrThreshold: array (nullable = true)
| | | |-- element: double (containsNull = true)
| | |-- numCategories: integer (nullable = true)
None
Out[47]:
['{"treeID":0,"nodeData":{"id":0,"prediction":0.8842198419237695,"impurity":0.21815527114830424,"impurityStats":[32769.0,28975.0,32769.0],"gain":8.527167006990577E-4,"leftChild":1,"rightChild":28,"split":{"featureIndex":7,"leftCategoriesOrThreshold":[68840.0],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":1,"prediction":0.8014340871483728,"impurity":0.35770340395665423,"impurityStats":[3626.0,2906.0,3626.0],"gain":0.00441849734961669,"leftChild":2,"rightChild":17,"split":{"featureIndex":3,"leftCategoriesOrThreshold":[117961.0],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":2,"prediction":0.9060402684563759,"impurity":0.1790910319354984,"impurityStats":[1043.0,945.0,1043.0],"gain":0.001958666596512771,"leftChild":3,"rightChild":10,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[38391.5],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":3,"prediction":0.8755304101838756,"impurity":0.23344650084325466,"impurityStats":[707.0,619.0,707.0],"gain":0.002761858856499433,"leftChild":4,"rightChild":7,"split":{"featureIndex":1,"leftCategoriesOrThreshold":[50586.5],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":4,"prediction":0.9153674832962138,"impurity":0.1621023705239557,"impurityStats":[449.0,411.0,449.0],"gain":0.004636673637323372,"leftChild":5,"rightChild":6,"split":{"featureIndex":2,"leftCategoriesOrThreshold":[118076.5],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":5,"prediction":0.9300699300699301,"impurity":0.13496992517971543,"impurityStats":[429.0,399.0,429.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":6,"prediction":0.6,"impurity":0.64,"impurityStats":[20.0,12.0,20.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":7,"prediction":0.8062015503875969,"impurity":0.35003906015263503,"impurityStats":[258.0,208.0,258.0],"gain":0.04252051859351158,"leftChild":8,"rightChild":9,"split":{"featureIndex":1,"leftCategoriesOrThreshold":[51021.5],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":8,"prediction":-0.42857142857142855,"impurity":0.8163265306122449,"impurityStats":[7.0,-3.0,7.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":9,"prediction":0.8406374501992032,"impurity":0.2933286773225822,"impurityStats":[251.0,211.0,251.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":10,"prediction":0.9702380952380952,"impurity":0.058638038548752866,"impurityStats":[336.0,326.0,336.0],"gain":0.001888889943465373,"leftChild":11,"rightChild":14,"split":{"featureIndex":2,"leftCategoriesOrThreshold":[117939.0],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":11,"prediction":0.9864406779661017,"impurity":0.02693478885377764,"impurityStats":[295.0,291.0,295.0],"gain":5.434175132537113E-4,"leftChild":12,"rightChild":13,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[81527.5],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":12,"prediction":0.9927272727272727,"impurity":0.014492561983471123,"impurityStats":[275.0,273.0,275.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":13,"prediction":0.9,"impurity":0.19000000000000003,"impurityStats":[20.0,18.0,20.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":14,"prediction":0.8536585365853658,"impurity":0.27126710291493156,"impurityStats":[41.0,35.0,41.0],"gain":0.030321057090836934,"leftChild":15,"rightChild":16,"split":{"featureIndex":4,"leftCategoriesOrThreshold":[117943.0],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":15,"prediction":0.9393939393939394,"impurity":0.11753902662993572,"impurityStats":[33.0,31.0,33.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":16,"prediction":0.5,"impurity":0.75,"impurityStats":[8.0,4.0,8.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":17,"prediction":0.759194734804491,"impurity":0.4236233546451387,"impurityStats":[2583.0,1961.0,2583.0],"gain":0.004044748388455199,"leftChild":18,"rightChild":21,"split":{"featureIndex":6,"leftCategoriesOrThreshold":[90233.0],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":18,"prediction":0.0,"impurity":1.0,"impurityStats":[18.0,0.0,18.0],"gain":1.0,"leftChild":19,"rightChild":20,"split":{"featureIndex":1,"leftCategoriesOrThreshold":[1312.0],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":19,"prediction":1.0,"impurity":0.0,"impurityStats":[9.0,9.0,9.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":20,"prediction":-1.0,"impurity":0.0,"impurityStats":[9.0,-9.0,9.0],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":0,"nodeData":{"id":21,"prediction":0.764522417153996,"impurity":0.41550547366901114,"impurityStats":[2565.0,1961.0,2565.0],"gain":0.00347330975217737,"leftChild":22,"rightChild":25,"split":{"featureIndex":3,"leftCategoriesOrThreshold":[118591.5],"numCategories":-1}}}',
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'{"treeID":16,"nodeData":{"id":7,"prediction":0.28313957823383246,"impurity":5.479447160903114E-4,"impurityStats":[12.0,3.3976749388059897,0.9685915857422742],"gain":3.464765383924482E-4,"leftChild":8,"rightChild":9,"split":{"featureIndex":0,"leftCategoriesOrThreshold":[4676.0],"numCategories":-1}}}',
'{"treeID":16,"nodeData":{"id":8,"prediction":0.24151767664391993,"impurity":9.6131345252784E-6,"impurityStats":[2.0,0.48303535328783986,0.11668080253200469],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
'{"treeID":16,"nodeData":{"id":9,"prediction":0.29146395855181506,"impurity":2.398391863323801E-4,"impurityStats":[10.0,2.9146395855181506,0.8519107832102655],"gain":-1.0,"leftChild":-1,"rightChild":-1,"split":{"featureIndex":-1,"leftCategoriesOrThreshold":[],"numCategories":-1}}}',
...]
In [48]:
rf = RandomForestClassifier(featuresCol="indexedFeatures", labelCol="ACTION", maxDepth=5, maxBins=255)
pipeline = Pipeline(stages=[featureIndexer, rf])
model = pipeline.fit(vtrain_df)
In [49]:
print(model.stages[1].getNumTrees)
print(model.stages[1].featureImportances)
20
(9,[0,1,2,3,4,5,6,7,8],[0.08164850994188799,0.12823295679547997,0.1338880773755482,0.12371862709347889,0.08642789982885535,0.08021952673874957,0.13384281361660819,0.15129559911878582,0.08072598949060586])
In [50]:
model.stages[1].save('AmazonRF')
model_df = spark.read.parquet("AmazonRF/data")
print(model_df.printSchema())
model_df.toJSON().collect()
root
|-- treeID: integer (nullable = true)
|-- nodeData: struct (nullable = true)
| |-- id: integer (nullable = true)
| |-- prediction: double (nullable = true)
| |-- impurity: double (nullable = true)
| |-- impurityStats: array (nullable = true)
| | |-- element: double (containsNull = true)
| |-- gain: double (nullable = true)
| |-- leftChild: integer (nullable = true)
| |-- rightChild: integer (nullable = true)
| |-- split: struct (nullable = true)
| | |-- featureIndex: integer (nullable = true)
| | |-- leftCategoriesOrThreshold: array (nullable = true)
| | | |-- element: double (containsNull = true)
| | |-- numCategories: integer (nullable = true)
None
Out[50]:
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...]
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