sklearn.ensemble.RandomForestClassifier.basic

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer

cancer=load_breast_cancer()
X_train,X_test,y_train,y_test=train_test_split(cancer.data,cancer.target)

 

 

from sklearn.ensemble import RandomForestClassifier

 

 

model=RandomForestClassifier(n_estimators=100)
model.fit(X_train,y_train)

train_score=model.score(X_train,y_train)
test_score=model.score(X_test,y_test)
display(train_score,test_score)

# 1.0
# 0.916083916083916

 

trees=model.estimators_
display(trees)

'''
[DecisionTreeClassifier(class_weight=None, criterion='gini', 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, presort=False,
             random_state=132230704, splitter='best'),
 DecisionTreeClassifier(class_weight=None, criterion='gini', 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, presort=False,
             random_state=1424136292, splitter='best'),
            . 
            .
            .
            .
'''

 

 

model

'''
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            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=100, n_jobs=1,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False)
            
'''

 

result=np.zeros([143,100])
for i in range(100):
    result[:,i]=trees[i].predict(X_test)
result

'''
array([[0., 1., 1., ..., 0., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [1., 1., 1., ..., 1., 1., 1.],
       [0., 0., 0., ..., 0., 1., 0.]])
       
'''