Random Forest And Extra Trees

随机森林

我们对使用决策树随机取样的集成学习有个形象的名字–随机森林。

scikit-learn 中封装的随机森林，在决策树的节点划分上，在随机的特征子集上寻找最优划分特征。

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets

X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=666)

plt.scatter(X[y==0, 0], X[y==0, 1])
plt.scatter(X[y==1, 0], X[y==1, 1])
plt.show()

from sklearn.ensemble import RandomForestClassifier

rf_clf = RandomForestClassifier(n_estimators=500, random_state=666, oob_score=True)
rf_clf.fit(X, y)

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_estimat 大专栏  Random Forest And Extra Treesors=500, n_jobs=1, oob_score=True, random_state=666, verbose=0, warm_start=False)

rf_clf.oob_score_

0.892

自定义决策树某些参数

rf_clf2 = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16
                                , random_state=666, oob_score=True)
rf_clf2.fit(X, y)
rf_clf2.oob_score_

0.906

Extra-Trees

在决策树的节点划分上，使用随机的特征和随机的阈值。

随机性更加极端。

提供了额外的随机性，一直过拟合，但增大了 bias 。

更快的训练速度。

from sklearn.ensemble import ExtraTreesClassifier

et_clf = ExtraTreesClassifier(n_estimators=500, bootstrap=True
                              , random_state=666, oob_score=True)
et_clf.fit(X, y)

ExtraTreesClassifier(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=500, n_jobs=1, oob_score=True, random_state=666, verbose=0, warm_start=False)

et_clf.oob_score_

0.892

集成学习解决回归问题

from sklearn.ensemble import BaggingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesRegressor