[AI]/python.sklearn
sklearn.ensemble.RandomForestClassifier.basic
givemebro
2020. 4. 20. 14:49
반응형
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.]])
'''
반응형