In [1]:

    

import os

import pandas as pd
import numpy as np

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split


    

In [2]:

    

pd.set_option('max_columns', 200)
pd.set_option('max_rows', 200)
pd.set_option('display.float_format', '{:.2f}'.format)


    

In [3]:

    

df = pd.read_pickle('../Chapter 7 - Data Preparation and Visualization/claims_df')


    

In [4]:

    

forest = RandomForestRegressor(n_estimators=1, random_state=314)


    

In [5]:

    

disease = ['SP_ALZHDMTA','SP_CHF', 'SP_CHRNKIDN', 'SP_CNCR', 'SP_COPD', 'SP_DEPRESSN','SP_DIABETES', 'SP_ISCHMCHT', 'SP_OSTEOPRS', 'SP_RA_OA', 'SP_STRKETIA']
gender = ['gender_2']
ESRD = ['ESRD_Y']


    

In [6]:

    

X = df[disease+gender+ESRD]
y = df.TOTAL_LOG_PAID


    

In [7]:

    

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=314)


    

In [8]:

    

X_train.shape


    

    
Out[8]:





(2820, 13)

In [9]:

    

forest.fit(X_train, y_train)


    

    
Out[9]:





RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, n_estimators=1, n_jobs=1,
           oob_score=False, random_state=314, verbose=0, warm_start=False)

In [10]:

    

y_pred = pd.DataFrame(forest.predict(X_test), columns=['y_pred'])


    

In [13]:

    

y_pred.plot();


    

In [14]:

    

first_tree = forest.estimators_[0]


    

In [15]:

    

feat_imp = pd.Series(forest.feature_importances_, index=X.columns)


    

In [16]:

    

feat_imp.sort_values(ascending=False)


    

    
Out[16]:





SP_ISCHMCHT   0.33
SP_CHRNKIDN   0.14
SP_DIABETES   0.12
SP_DEPRESSN   0.07
SP_ALZHDMTA   0.06
SP_CHF        0.06
SP_RA_OA      0.05
gender_2      0.04
SP_OSTEOPRS   0.04
SP_CNCR       0.03
SP_STRKETIA   0.03
SP_COPD       0.02
ESRD_Y        0.02
dtype: float64

In [17]:

    

forest.predict(X_test)


    

    
Out[17]:





array([7.25443132, 8.02943284, 5.6002005 , ..., 5.6002005 , 7.88246977,
       8.03281136])

In [18]:

    

forest.score(X_train, y_train)


    

    
Out[18]:





0.5615344687244734

In [19]:

    

forest.score(X_test, y_test)


    

    
Out[19]:





0.3597939457853613

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