机器学习实战-用递归方法创建树

最新推荐文章于 2025-09-22 23:11:09 发布

原创最新推荐文章于 2025-09-22 23:11:09 发布 · 534 阅读

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本文深入讲解了决策树算法的核心原理及实现过程。通过定义createTree函数来递归地创建决策树，选择最佳划分特征，并根据该特征的不同取值继续划分数据集。当所有样本属于同一类别或无法再进行特征划分时，递归终止。

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def createTree(dataSet,labels):
    classList = [example[-1] for example in dataSet]
 #设置递归停止条件
    if classList.count(classList[0]) == len(classList): 
        return classList[0]#stop splitting when all of the classes are equal
    if len(dataSet[0]) == 1: #stop splitting when there are no more features in dataSet
        return majorityCnt(classList)
    bestFeat = chooseBestFeatureToSplit(dataSet)
    bestFeatLabel = labels[bestFeat]
    myTree = {bestFeatLabel:{}}
    del(labels[bestFeat])
    featValues = [example[bestFeat] for example in dataSet]
    uniqueVals = set(featValues)
 #递归处理   
     for value in uniqueVals:
        subLabels = labels[:]       #copy all of labels, so trees don't mess up existing labels
        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)
    return myTree

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