print(__doc__)importnumpyasnpimportmatplotlib.pyplotaspltfromsklearn.datasetsimportload_irisfromsklearn.treeimportDecisionTreeClassifier, plot_tree# Parametersn_classes =3plot_colors ="ryb"plot_step =0.02# Load datairis = load_iris()forpairidx, pairinenumerate([[0,1], [0,2], [0,3], [1...
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=...
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,presort...
用法: 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, class_weight=None, ccp_alpha=...
【风控】巧用sklearn的DecisionTreeClassifier寻找切分点做决策树 JiaX 风控数据分析师,Python爱好者一枚 背景 在业务中经常遇到多个特征或评分用作决策树,但很多时候如何进行交叉、如何决定切分点等关键性问题,都需要经验判断以及慢慢尝试调整,花费较大时间精力。本文尝试借用sklearn库中的DecisionTreeClassifier决策树算法...
clf = DecisionTreeClassifier(max_depth=15) rng = np.random.RandomState(random_state) i = np.arange(len(y)) rng.shuffle(i) visualize_tree(clf, X[i[:250]], y[i[:250]], boundaries=False, xlim=(X[:, 0].min(), X[:, 0].max()), ...
sklearn DecisionTree 源码分析 sklearn.tree._classes.BaseDecisionTree#fit y至少为1维(意思是可以处理multilabels数据) y = np.atleast_1d(y) 1. if is_classifier(self): self.tree_ = Tree(self.n_features_, self.n_classes_, self.n_outputs_)...
sklearn.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,presort=False) ...
sklearn.tree.DecisionTreeClassifier()函数⽤于构建决策树，默认使⽤CART算法，现对该函数参数进⾏说明，参考的是scikit-learn 0.20.3版本。sklearn.tree.DecisionTreeClassifier(criterion=’gini’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf...
，分别对应的模型为分类决策树模型（DecisionTreeClassifier）及回归决策树模型（DecisionTreeRegressor）。