In [1]:
from sklearn.datasets import load_iris
In [2]:
iris = load_iris()
In [5]:
print iris.feature_names
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
In [6]:
print iris.target_names
['setosa' 'versicolor' 'virginica']
In [7]:
print iris.data[0]
[ 5.1 3.5 1.4 0.2]
In [8]:
print iris.data[149]
[ 5.9 3. 5.1 1.8]
In [9]:
print iris.target[0],iris.target[50],iris.target[149]
0 1 2
In [10]:
for i in range(len(iris.target)):
print "Example %d: label %s, features %s" % (i,iris.target[i],iris.data[i])
Example 0: label 0, features [ 5.1 3.5 1.4 0.2]
Example 1: label 0, features [ 4.9 3. 1.4 0.2]
Example 2: label 0, features [ 4.7 3.2 1.3 0.2]
Example 3: label 0, features [ 4.6 3.1 1.5 0.2]
Example 4: label 0, features [ 5. 3.6 1.4 0.2]
Example 5: label 0, features [ 5.4 3.9 1.7 0.4]
Example 6: label 0, features [ 4.6 3.4 1.4 0.3]
Example 7: label 0, features [ 5. 3.4 1.5 0.2]
Example 8: label 0, features [ 4.4 2.9 1.4 0.2]
Example 9: label 0, features [ 4.9 3.1 1.5 0.1]
Example 10: label 0, features [ 5.4 3.7 1.5 0.2]
Example 11: label 0, features [ 4.8 3.4 1.6 0.2]
Example 12: label 0, features [ 4.8 3. 1.4 0.1]
Example 13: label 0, features [ 4.3 3. 1.1 0.1]
Example 14: label 0, features [ 5.8 4. 1.2 0.2]
Example 15: label 0, features [ 5.7 4.4 1.5 0.4]
Example 16: label 0, features [ 5.4 3.9 1.3 0.4]
Example 17: label 0, features [ 5.1 3.5 1.4 0.3]
Example 18: label 0, features [ 5.7 3.8 1.7 0.3]
Example 19: label 0, features [ 5.1 3.8 1.5 0.3]
Example 20: label 0, features [ 5.4 3.4 1.7 0.2]
Example 21: label 0, features [ 5.1 3.7 1.5 0.4]
Example 22: label 0, features [ 4.6 3.6 1. 0.2]
Example 23: label 0, features [ 5.1 3.3 1.7 0.5]
Example 24: label 0, features [ 4.8 3.4 1.9 0.2]
Example 25: label 0, features [ 5. 3. 1.6 0.2]
Example 26: label 0, features [ 5. 3.4 1.6 0.4]
Example 27: label 0, features [ 5.2 3.5 1.5 0.2]
Example 28: label 0, features [ 5.2 3.4 1.4 0.2]
Example 29: label 0, features [ 4.7 3.2 1.6 0.2]
Example 30: label 0, features [ 4.8 3.1 1.6 0.2]
Example 31: label 0, features [ 5.4 3.4 1.5 0.4]
Example 32: label 0, features [ 5.2 4.1 1.5 0.1]
Example 33: label 0, features [ 5.5 4.2 1.4 0.2]
Example 34: label 0, features [ 4.9 3.1 1.5 0.1]
Example 35: label 0, features [ 5. 3.2 1.2 0.2]
Example 36: label 0, features [ 5.5 3.5 1.3 0.2]
Example 37: label 0, features [ 4.9 3.1 1.5 0.1]
Example 38: label 0, features [ 4.4 3. 1.3 0.2]
Example 39: label 0, features [ 5.1 3.4 1.5 0.2]
Example 40: label 0, features [ 5. 3.5 1.3 0.3]
Example 41: label 0, features [ 4.5 2.3 1.3 0.3]
Example 42: label 0, features [ 4.4 3.2 1.3 0.2]
Example 43: label 0, features [ 5. 3.5 1.6 0.6]
Example 44: label 0, features [ 5.1 3.8 1.9 0.4]
Example 45: label 0, features [ 4.8 3. 1.4 0.3]
Example 46: label 0, features [ 5.1 3.8 1.6 0.2]
Example 47: label 0, features [ 4.6 3.2 1.4 0.2]
Example 48: label 0, features [ 5.3 3.7 1.5 0.2]
Example 49: label 0, features [ 5. 3.3 1.4 0.2]
Example 50: label 1, features [ 7. 3.2 4.7 1.4]
Example 51: label 1, features [ 6.4 3.2 4.5 1.5]
Example 52: label 1, features [ 6.9 3.1 4.9 1.5]
Example 53: label 1, features [ 5.5 2.3 4. 1.3]
Example 54: label 1, features [ 6.5 2.8 4.6 1.5]
Example 55: label 1, features [ 5.7 2.8 4.5 1.3]
Example 56: label 1, features [ 6.3 3.3 4.7 1.6]
Example 57: label 1, features [ 4.9 2.4 3.3 1. ]
Example 58: label 1, features [ 6.6 2.9 4.6 1.3]
Example 59: label 1, features [ 5.2 2.7 3.9 1.4]
Example 60: label 1, features [ 5. 2. 3.5 1. ]
Example 61: label 1, features [ 5.9 3. 4.2 1.5]
Example 62: label 1, features [ 6. 2.2 4. 1. ]
Example 63: label 1, features [ 6.1 2.9 4.7 1.4]
Example 64: label 1, features [ 5.6 2.9 3.6 1.3]
Example 65: label 1, features [ 6.7 3.1 4.4 1.4]
Example 66: label 1, features [ 5.6 3. 4.5 1.5]
Example 67: label 1, features [ 5.8 2.7 4.1 1. ]
Example 68: label 1, features [ 6.2 2.2 4.5 1.5]
Example 69: label 1, features [ 5.6 2.5 3.9 1.1]
Example 70: label 1, features [ 5.9 3.2 4.8 1.8]
Example 71: label 1, features [ 6.1 2.8 4. 1.3]
Example 72: label 1, features [ 6.3 2.5 4.9 1.5]
Example 73: label 1, features [ 6.1 2.8 4.7 1.2]
Example 74: label 1, features [ 6.4 2.9 4.3 1.3]
Example 75: label 1, features [ 6.6 3. 4.4 1.4]
Example 76: label 1, features [ 6.8 2.8 4.8 1.4]
Example 77: label 1, features [ 6.7 3. 5. 1.7]
Example 78: label 1, features [ 6. 2.9 4.5 1.5]
Example 79: label 1, features [ 5.7 2.6 3.5 1. ]
Example 80: label 1, features [ 5.5 2.4 3.8 1.1]
Example 81: label 1, features [ 5.5 2.4 3.7 1. ]
Example 82: label 1, features [ 5.8 2.7 3.9 1.2]
Example 83: label 1, features [ 6. 2.7 5.1 1.6]
Example 84: label 1, features [ 5.4 3. 4.5 1.5]
Example 85: label 1, features [ 6. 3.4 4.5 1.6]
Example 86: label 1, features [ 6.7 3.1 4.7 1.5]
Example 87: label 1, features [ 6.3 2.3 4.4 1.3]
Example 88: label 1, features [ 5.6 3. 4.1 1.3]
Example 89: label 1, features [ 5.5 2.5 4. 1.3]
Example 90: label 1, features [ 5.5 2.6 4.4 1.2]
Example 91: label 1, features [ 6.1 3. 4.6 1.4]
Example 92: label 1, features [ 5.8 2.6 4. 1.2]
Example 93: label 1, features [ 5. 2.3 3.3 1. ]
Example 94: label 1, features [ 5.6 2.7 4.2 1.3]
Example 95: label 1, features [ 5.7 3. 4.2 1.2]
Example 96: label 1, features [ 5.7 2.9 4.2 1.3]
Example 97: label 1, features [ 6.2 2.9 4.3 1.3]
Example 98: label 1, features [ 5.1 2.5 3. 1.1]
Example 99: label 1, features [ 5.7 2.8 4.1 1.3]
Example 100: label 2, features [ 6.3 3.3 6. 2.5]
Example 101: label 2, features [ 5.8 2.7 5.1 1.9]
Example 102: label 2, features [ 7.1 3. 5.9 2.1]
Example 103: label 2, features [ 6.3 2.9 5.6 1.8]
Example 104: label 2, features [ 6.5 3. 5.8 2.2]
Example 105: label 2, features [ 7.6 3. 6.6 2.1]
Example 106: label 2, features [ 4.9 2.5 4.5 1.7]
Example 107: label 2, features [ 7.3 2.9 6.3 1.8]
Example 108: label 2, features [ 6.7 2.5 5.8 1.8]
Example 109: label 2, features [ 7.2 3.6 6.1 2.5]
Example 110: label 2, features [ 6.5 3.2 5.1 2. ]
Example 111: label 2, features [ 6.4 2.7 5.3 1.9]
Example 112: label 2, features [ 6.8 3. 5.5 2.1]
Example 113: label 2, features [ 5.7 2.5 5. 2. ]
Example 114: label 2, features [ 5.8 2.8 5.1 2.4]
Example 115: label 2, features [ 6.4 3.2 5.3 2.3]
Example 116: label 2, features [ 6.5 3. 5.5 1.8]
Example 117: label 2, features [ 7.7 3.8 6.7 2.2]
Example 118: label 2, features [ 7.7 2.6 6.9 2.3]
Example 119: label 2, features [ 6. 2.2 5. 1.5]
Example 120: label 2, features [ 6.9 3.2 5.7 2.3]
Example 121: label 2, features [ 5.6 2.8 4.9 2. ]
Example 122: label 2, features [ 7.7 2.8 6.7 2. ]
Example 123: label 2, features [ 6.3 2.7 4.9 1.8]
Example 124: label 2, features [ 6.7 3.3 5.7 2.1]
Example 125: label 2, features [ 7.2 3.2 6. 1.8]
Example 126: label 2, features [ 6.2 2.8 4.8 1.8]
Example 127: label 2, features [ 6.1 3. 4.9 1.8]
Example 128: label 2, features [ 6.4 2.8 5.6 2.1]
Example 129: label 2, features [ 7.2 3. 5.8 1.6]
Example 130: label 2, features [ 7.4 2.8 6.1 1.9]
Example 131: label 2, features [ 7.9 3.8 6.4 2. ]
Example 132: label 2, features [ 6.4 2.8 5.6 2.2]
Example 133: label 2, features [ 6.3 2.8 5.1 1.5]
Example 134: label 2, features [ 6.1 2.6 5.6 1.4]
Example 135: label 2, features [ 7.7 3. 6.1 2.3]
Example 136: label 2, features [ 6.3 3.4 5.6 2.4]
Example 137: label 2, features [ 6.4 3.1 5.5 1.8]
Example 138: label 2, features [ 6. 3. 4.8 1.8]
Example 139: label 2, features [ 6.9 3.1 5.4 2.1]
Example 140: label 2, features [ 6.7 3.1 5.6 2.4]
Example 141: label 2, features [ 6.9 3.1 5.1 2.3]
Example 142: label 2, features [ 5.8 2.7 5.1 1.9]
Example 143: label 2, features [ 6.8 3.2 5.9 2.3]
Example 144: label 2, features [ 6.7 3.3 5.7 2.5]
Example 145: label 2, features [ 6.7 3. 5.2 2.3]
Example 146: label 2, features [ 6.3 2.5 5. 1.9]
Example 147: label 2, features [ 6.5 3. 5.2 2. ]
Example 148: label 2, features [ 6.2 3.4 5.4 2.3]
Example 149: label 2, features [ 5.9 3. 5.1 1.8]
In [11]:
import numpy as np
In [12]:
test_idx = [0,50,100]
In [14]:
#training data
train_target = np.delete(iris.target,test_idx)
train_data = np.delete(iris.data,test_idx,axis=0)
In [16]:
#testing data
test_target = iris.target[test_idx]
test_data = iris.data[test_idx]
In [17]:
from sklearn import tree
clf = tree.DecisionTreeClassifier()
In [18]:
clf = clf.fit(train_data,train_target)
In [19]:
print test_target
[0 1 2]
In [20]:
print clf.predict(test_data)
[0 1 2]
In [ ]:
Content source: MarcelloCacciato/MachineLearning
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