决策树 参数

tree/example.py

@@ -4,7 +4,7 @@
 from sklearn import tree
 from sklearn.datasets import load_wine
 from sklearn.model_selection import train_test_split
-
+import numpy
 import graphviz
 
 wine = load_wine()
@@ -18,12 +18,16 @@ print(wine.feature_names)
 print(wine.target_names)
 
 Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)
-clf = tree.DecisionTreeClassifier(criterion="entropy")#实例化，criterion不写的话默认是基尼系数
+numpy.savetxt("foo.csv", Xtrain, delimiter=",")
+
+clf = tree.DecisionTreeClassifier(criterion="entropy", max_features=1, max_depth=5)#实例化，criterion不写的话默认是基尼系数
+# clf.n_features_ = 2
 clf = clf.fit(Xtrain, Ytrain)
 score = clf.score(Xtest, Ytest) #返回预测的准确度
 print("score:", score)
 
-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', '脯氨酸']
+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', '脯氨酸']
 
 
 dot_data = tree.export_graphviz(clf
@@ -35,6 +39,6 @@ dot_data = tree.export_graphviz(clf
                                 )
 dot_data = dot_data.replace('helvetica', '"Microsoft YaHei"')
 graph = graphviz.Source(dot_data)
-graph.render("Tree")
+graph.render("Tree1")
 graph  # graph.view()