更新决策树的内容，做ppt

src/python/03.DecisionTree/DTSklearn.py

@@ -51,7 +51,7 @@ def predict_train(x_train, y_train):
     return y_pre, clf
 
 
-def show_precision_recall(x, clf,  y_train, y_pre):
+def show_precision_recall(x, y, clf,  y_train, y_pre):
     '''
     准确率与召回率
     参考链接： http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve
@@ -110,7 +110,7 @@ if __name__ == '__main__':
     y_pre, clf = predict_train(x_train, y_train)
 
     # 展现 准确率与召回率
-    show_precision_recall(x, clf, y_train, y_pre)
+    show_precision_recall(x, y, clf, y_train, y_pre)
 
     # 可视化输出
     show_pdf(clf)

src/python/03.DecisionTree/DecisionTree.py

@@ -7,9 +7,9 @@ Update on 2017-02-27
 Decision Tree Source Code for Machine Learning in Action Ch. 3
 @author: Peter Harrington/jiangzhonglian
 '''
-from math import log
 import operator
-import DecisionTreePlot as dtPlot
+from math import log
+import decisionTreePlot as dtPlot
 
 
 def createDataSet():
@@ -130,7 +130,9 @@ def chooseBestFeatureToSplit(dataSet):
             prob = len(subDataSet)/float(len(dataSet))
             newEntropy += prob * calcShannonEnt(subDataSet)
         # gain[信息增益] 值越大，意味着该分类提供的信息量越大，该特征对分类的不确定程度越小
+        # 也就说： 列进行group分组后，对应的类别越多，信息量越大，那么香农熵越小，那么信息增益就越大，所以gain越大
         infoGain = baseEntropy - newEntropy
+        # print 'infoGain=', infoGain, 'bestFeature=', i
         if (infoGain > bestInfoGain):
             bestInfoGain = infoGain
             bestFeature = i

src/python/03.DecisionTree/DecisionTreePlot.py

@@ -128,5 +128,5 @@ def retrieveTree(i):
     return listOfTrees[i]
 
 
-myTree = retrieveTree(0)
+myTree = retrieveTree(1)
 createPlot(myTree)