Breiman, “Random Forests”, Machine Learning,45(1),5-32,2001. Methods predict(X) Predict class for X. The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probabi...
RF_class=ensemble.RandomForestClassifier(n_estimators=200,random_state=1234)#随机森林的拟合RF_class.fit(X_train,y_train)#模型在测试集上的预测RFclass_pred=RF_class.predict(X_test)#模型的准确率print('模型在测试集的预测准确率为:',metrics.accuracy_score(y_test,RFclass_pred))#计算绘图数据y_sco...
sklearn.ensemble.RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False,...
http://sklearn.apachecn.org/cn/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier
class sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score...
RandomForestClassifier 代码语言:javascript 复制 classsklearn.ensemble.RandomForestClassifier(n_estimators=’10’,criterion=’gini’,max_depth=None,min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_features=’auto’,max_leaf_nodes=None,min_impurity_decrease=0.0,min_impurity_spli...
self.classifier = SklearnClassifier(RandomForestClassifier( n_estimators=self.numTrees),sparse=False) train_set1,test_set,train_set2 = feature_sets[:i],feature_sets[i],feature_sets[i+1:] train_set = train_set1+train_set2 test_set = [test_set] ...
predict(X_test), y_true=y_test) ## Random Forest with tunned parameters RF_tunned_test_mse = mean_squared_error(y_pred=RF_classifier.predict(X_test), y_true=y_test) 要实际查看已调整模型和未调整模型之间的比较,我们可以看到均方误差的值。 以下屏幕截图显示了用于获取两个模型的mean_squared_...
max_leaf_nodes int, default=None最大叶子节点个数,默认无限多 min_samples_leaf 叶子节点的最小样本数,默认为1 random_state 随机数种子,一般要设置。 n_jobs 并行计算的个数,默认为1 #随机森林回归 from sklearn.ensemble import RandomForestRegressor rf=RandomForestRegressor(random_state=42) rf.fit(train...
# 需要導入模塊: import sklearn [as 別名]# 或者: from sklearn importneighbors[as 別名]defrun_sklearn():n_trees =100n_folds =3# https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/alg_list = [ ['rforest',RandomForestClassifier(n_estimators=1000, n_jobs=-1, verbos...