EL之RF(随机性的Bagging+DTR)：利用随机选择属性的bagging方法解决回归(对多变量的数据集+实数值评分预测)问题

EL之RF(随机性的Bagging+DTR)：利用随机选择属性的bagging方法解决回归(对多变量的数据集+实数值评分预测)问题

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输出结果

设计思路

核心代码

for iTrees in range(numTreesMax):

    modelList.append(DecisionTreeRegressor(max_depth=treeDepth))

    #第一个随机：随机抽取属性样本
    idxAttr = random.sample(range(ncols), nAttr)
    idxAttr.sort()
    indexList.append(idxAttr)

    #第二个随机：随机抽取训练行样本
    idxRows = []
    for i in range(int(0.5 * nTrainRows)):
        idxRows.append(random.choice(range(len(xTrain))))
    idxRows.sort()

    xRfTrain = []
    yRfTrain = []

    for i in range(len(idxRows)):
        temp = [xTrain[idxRows[i]][j] for j in idxAttr]
        xRfTrain.append(temp)
        yRfTrain.append(yTrain[idxRows[i]])

    modelList[-1].fit(xRfTrain, yRfTrain)

    xRfTest = []
    for xx in xTest:
        temp = [xx[i] for i in idxAttr]
        xRfTest.append(temp)

    latestOutSamplePrediction = modelList[-1].predict(xRfTest)
    predList.append(list(latestOutSamplePrediction))