tree_fit.classes_#array([0, 1, 2])tree_fit.feature_importances_#array([0.42708333, 0.57291667])tree_fit.max_features_#2tree_fit.n_classes_#3tree_fit.n_features_#2tree_fit.n_outputs_#1tree_fit.tree_ 决策树可视化: pydotplus的安装:在命令行输入conda install -c conda-forge pydotplus ## ...
clf=DecisionTreeClassifier(criterion="entropy").fit(xtrain,ytrain) # 返回预测的准确度 accuracy score=clf.score(xtest,ytest)# 0.9629629629629629 feature_name=['酒精','苹果酸','灰','灰的碱性','镁','总酚','类黄酮','非黄烷类酚类','花青素','颜色强度','色调','od280/od315稀释葡萄酒',...
clf = DecisionTreeClassifier(max_depth=3, random_state=42)clf.fit(X, y)2. 模型可视化 决策树的可视化有助于理解模型的决策逻辑。可以使用graphviz库配合scikit-learn的export_graphviz函数绘制决策树图形：from sklearn.tree import export_graphviz import graphviz dot_data = export_graphviz(clf, out_file=...
tree.DecisionTreeClassifier 分类树的建模代码如下: fromsklearnimporttree# 导入需要的模块clf=tree.DecisionTreeClassifier()# 实例化clf=clf.fit(x_train,y_train)# 用训练数据集训练模型result=clf.score(x_test,y_test)# 导入测试集,从接口中调用需要的信息 3、分类树DecisionTreeClassifier 的参数介绍 sklear...
在windows CMD中输出中文是比较烦的事情，最简单的就是增加一个windows.py，用的时候import一下: #!/...
from sklearn.tree import DecisionTreeClassifier # 实例化决策树分类器,并指定一些参数 clf = DecisionTreeClassifier( criterion='entropy', # 'entropy' 表示使用信息增益来衡量分裂质量,选择信息增益最大的特征进行分裂 max_depth=5, # 限制决策树的最大深度为5,以防止过拟合(树不允许深度超过5层) ...
plt.title("Feature importances by DecisionTreeClassifier") plt.bar(range(len(indices)), importances[indices], color='lightblue', align="center") plt.step(range(len(indices)), np.cumsum(importances[indices]), where='mid', label='Cumulative') ...
clf=tree.DecisionTreeClassifier(criterion='entropy',random_state=200,splitter='random',max_depth=3,min_samples_leaf=10,min_samples_split=10)clf=clf.fit(x_train,y_train)dot_data=tree.export_graphviz(clf,feature_names=feature_name,class_names=['A酒','B酒','C酒'],filled=True,...
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_...
1. DecisionTreeClassifier API 的使用 和其他分类器一样,DecisionTreeClassifier 需要两个数组作为输入: X: 训练数据,稀疏或稠密矩阵,大小为 [n_samples, n_features] Y: 类别标签,整型数组,大小为 [n_samples] fromsklearnimporttree X = [[0,0], [1,1]] ...