接下来使用sklearn中的决策树进行分类,并绘制决策边界,这里指定决策树最大深度为2: '''这是使用很多次的决策边界绘制函数'''defplot_decision_boundary(model,axis):x0,x1=np.meshgrid(np.linspace(axis[0],axis[1],int((axis[1]-axis[0])*100)).reshape(-1,1),np.linspace(axis[2],axis[3],int(...
5、绘制整个数据集上的决策树:最后,我们训练一个决策树模型,使用整个鸢尾花数据集,并使用plot_tree函数绘制整个决策树的结构图。绘制整个鸢尾花数据集上的决策树 plt.figure() clf = DecisionTreeClassifier().fit(iris.data, iris.target) plot_tree(clf, filled=True) plt.title("Decision tree trained on al...
iris=datasets.load_iris()iris_all=pd.DataFrame(data=iris.data,columns=iris.feature_names).copy()# target=iris.target iris_all['target']=iris.target # 为了方便可视化,仅使用两个特征 iris=iris_all.iloc[:,2:]sns.scatterplot(data=iris,x=iris.columns.values[0],y=iris.columns.values[1],hue...
from sklearn import tree clf = tree.DecisionTreeClassifier(max_leaf_nodes=n) clf_ = clf.fit(X, data_y) feature_names = X.columns class_name = clf_.classes_.astype(int).astype(str) def output_pdf(clf_, name): from sklearn import tree from sklearn.externals.six import StringIO impor...
criterion="gini", random_state=2) clf.fit(X, y)# Test export codefeature_names = ['first feat','sepal_width'] nodes = plot_tree(clf, feature_names=feature_names)assertlen(nodes) ==3assertnodes[0].get_text() == ("first feat <= 0.0\nentropy = 0.5\n""samples = 6\nvalue = ...
fromsklearn.datasetsimportload_irisfromsklearnimporttreeX, y =load_iris(return_X_y=True)clf=tree.DecisionTreeClassifier()clf=clf.fit(X, y)tree.plot_tree(feature_names=feature_names 分类算法(决策树,SVM,随机森林,逻辑回归) LogisticRegressionfromsklearn.datasetsimportload_irisimportnumpy as npfromsk...
tree.plot_tree(clf) 2.2 Graphviz形式输出决策树 也可以用 Graphviz 格式(export_graphviz)输出。 如果使用的是 conda 包管理器,可以用如下方式安装: conda install python-graphviz pip install graphviz 以下展示了用 Graphviz 输出上述从鸢尾花数据集得到的决策树,结果保存为iris.pdf ...
plotAUC(test_y,clf.predict_proba(test_x)[:,1],'DT')returnauc 开发者ID:ds-ga-1001-final,项目名称:project,代码行数:9,代码来源:decision_tree.py 示例2: programmer_2 ▲点赞 6▼ # 需要导入模块: from sklearn.tree import DecisionTreeClassifier [as 别名]# 或者: from sklearn.tree.Decision...
Feature valuesare preferred to becategorical. If the values are continuous then they are discretized(构建模型之前 离散化) prior to building the model. Records aredistributed recursively(基于 属性值 递归分布)on the basis of attribute values.
tree import export_graphviz import pydotplus dot_data = StringIO() export_graphviz(clf, out_file=dot_data, filled=True, rounded=True, special_characters=True, feature_names = feature_cols,class_names=['0','1']) graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) graph.write_png...