RandomForest
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2021-01-20
11:36:15
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RandomForest
#-*- coding:utf-8 -*-
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, Y = make_classification(n_samples=1500, n_features=4, n_informative=2, n_redundant=0, random_state=0,
shuffle=False)
X1,X2,Y1,Y2 = train_test_split( X, Y, test_size=0.2, random_state=0)
#随机森林的参数包含的是决策树和弱分类器的参数
clf = RandomForestClassifier( n_estimators=50, max_depth=3, random_state=43)
clf.fit(X1,Y1)
answer = clf.predict(X2)
print(clf.score(X1,Y1))
print(clf.score(X2,Y2))
#使用交叉验证其标准差获得最优参数
from sklearn.model_selection import cross_val_score
scores = cross_val_score(clf, X, Y, cv=3)
print(scores.std())
print(clf.feature_importances_)
作者：Eily_king
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