ccp_alphas, path.impurities clfs = [] for ccp_alpha in ccp_alphas: clf = DecisionTreeClassifier(random_state=0, ccp_alpha=ccp_alpha) clf.fit(X_train, y_train) clfs.append(clf) train_scores = [clf.score(X_train, y_train) for clf in clfs] test_scores = [clf.score(X_test, y_...
min_impurity_split、ccp_alpha1、DecisionTreeClassifier的参数参数说明备注 criterion 权衡划分质量的指标'gini'(默认):Gini指数'entropy':熵 splitter 划分方式:'best'(默认):在特征的所有划分点中找出最优值'random':在一些随机划分点中找最优分裂点 'best'适合样本量不大的时候,如果样本数据量非常大,推荐'ran...
CCP oPRP判断树 Decision Tree (c)
用来回归的树(Regression Tree)和用来分类的树(classification Tree)具有一定的相似性,不过其不同之处在于决定分裂(Split)的过程。 使用某些技术(Ensemble Methods),可以构建多个Decision Tree: Bagging Decision Tree:创建多个Decision Tree,通过替换训练集合,得到多个Decision Tree,最终得到一致的结果。 Random Forest Clas...
决策树(decision tree)是一种基本的分类与回归方法。 分类问题中,基于特征对实例进行分类的过程。 优点:模型具有可读性,分类速度快。 学习:利用训练数据,根据损失函数最小化的原则建立决策树模型。 预测:对新的数据,利用决策树模型进行分类。 决策树学习通常包括3个步骤:特征选择、决策树生成、决策树修剪。 Quinlan...
CCPDecisionTree Appendix3:DecisionTree 278 Q1.Doesthisstepinvolveahazardofsufficientriskandseverity towarrantitscontrol? YesNoNotaCCP Doesacontrolmeasureforthehazardexistatthisstep? YesNoModifythestep, processor product Iscontrol atthisstep necessaryYes ...
ccp_alpha:将选择成本复杂度最大且小于ccp_alpha的子树。默认情况下,不执行修剪。 可选函数: classes_:类标签(单输出问题)或类标签数组的列表(多输出问题)。 feature_importances_:特征重要度。 max_features_:max_features的推断值。 n_classes_:类数(用于单输出问题),或包含每个输出的类数的列表(用于多输出...
decision treeanomaly detectionanomaly classificationThis paper presents a new approach to classify six anomaly types of control chart patterns (CCP), of systematic pattern, cyclic pattern, upward shift, downward shift, upward trend, and downward trend. Current CCP recognition methods use either ...
is that pruned tree t i + 1 is obtained by pruning the subtrees that show the lowest increase in the apparent error (error in the training set) per pruned leaf. regarding step (ii), ccp chooses the smallest tree whose error (either on the pruning set or on cross-validation) is not ...
classsklearn.tree.DecisionTreeClassifier(*,criterion='gini',splitter='best',max_depth=None,min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_features=None,random_state=None,max_leaf_nodes=None,min_impurity_decrease=0.0,min_impurity_split=None,class_weight=None,ccp_alpha=...