决策树模型python实现代码

决策树模型的Python实现代码如下所示：

1. 创建决策树

def createTree(dataSet, labels):
    classList = [example[-1] for example in dataSet]
    if classList.count(classList[0]) == len(classList):
        return classList[0]
    if len(dataSet[0]) == 1:
        return majorityCnt(classList)
    bestFeat = chooseBestFeatureToSplit(dataSet)
    bestFeatLabel = labels[bestFeat]
    myTree = {bestFeatLabel: {}}
    del (labels[bestFeat])
    featValues = [example[bestFeat] for example in dataSet]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels)
    return myTree

2. 使用决策树进行分类

def classify(inputTree, 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

3. 存储和加载决策树

import pickle

def storeTree(inputTree, filename):
    with open(filename, 'wb') as fw:
        pickle.dump(inputTree, fw)

def grabTree(filename):
    with open(filename, 'rb') as fr:
        return pickle.load(fr)

以上是决策树模型的Python实现代码，其中包括了创建决策树、使用决策树进行分类、存储和加载决策树等功能。