吼吼，这样我们全部的决策树的东西就实践完毕了，祝大家学习工作愉快。
决策树完结篇 (三)
ee,featLabels,testVec):
firstStr = list(inputTree.keys())[0]
secondDict = inputTree[firstStr]
featIndex = featLabels.index(firstStr)
for key in secondDict.keys():
if testVec[featIndex] == key:
if type(secondDict[key]).__name__=='dict':
classLabel = classify(secondDict[key],featLabels,testVec)
else:
classLabel = secondDict[key]
return classLabel
myDat,labels = CreateDataSet()
print(calcShannonEnt(myDat))
print(splitDataSet(myDat, 1, 1))
print(chooseBestFeatureToSplit(myDat))
myTree = createTree(myDat, labels)
print(classify(myTree, labels, [1, 0]))
print(classify(myTree, labels, [1, 1]))