决策树算法example

tree/example.py

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+#!/usr/bin/python
+# -*- coding: UTF-8 -*-
+
+from sklearn import tree
+from sklearn.datasets import load_wine
+from sklearn.model_selection import train_test_split
+
+import graphviz
+
+wine = load_wine()
+print(wine.data.shape) #178*13
+print(wine.target)
+#如果wine是一张表，应该长这样：
+import pandas as pd
+pdata = pd.concat([pd.DataFrame(wine.data),pd.DataFrame(wine.target)],axis=1)
+
+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
+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']
+
+
+dot_data = tree.export_graphviz(clf
+                                # ,out_file = None
+                                , feature_names=feature_name
+                                , class_names=["琴酒", "雪莉", "贝尔摩德"]
+                                , filled=True  # 让树的每一块有颜色，颜色越浅，表示不纯度越高
+                                , rounded=True  # 树的块的形状
+                                )
+dot_data = dot_data.replace('helvetica', '"Microsoft YaHei"')
+graph = graphviz.Source(dot_data)
+graph.render("Tree")
+graph  # graph.view()
+