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,...
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...
13.decision_function_shape :‘ovo’, ‘ovr’ or None, default=‘ovr’ 14.random_state :数据洗牌时的种子值,int值 属性: 1.support_ 支持向量的索引 2.support_vectors_ 支持向量 3.n_support_ 每个类别的支持向量的数目 4.dual_coef_ : 一个数组,形状为[n_class-1,n_SV]。对偶问题中,在分类...
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_...
def Random_forest(features,target,test_size_percent=0.2,cv_split=3): X_array = features.as_matrix() y_array = target.as_matrix() model_rdf = RandomForestRegressor() X_train, X_test, y_train, y_test = train_test_split(X_array, y_array.T.squeeze(), test_size=test_size_percent,...
Also, as a sanity to check to see if there is something wrong with the data I tried the same but with a random forest classifier and here the problem disappears: # random forest clf = RandomForestClassifier(n_jobs=-1) param_grid = { 'min_samples_split': [3, 5, 10], 'n_estimator...
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