EL之Bagging(DTR)：利用Bagging对回归问题(实数值评分预测)建模(调2参)

EL之Bagging(DTR)：利用Bagging对回归问题(实数值评分预测)建模(调2参)

 

 

目录

输出结果

设计思路

核心代码

 

 

 

 

输出结果

 

设计思路

 

核心代码

bagFract = 1.0        #----------------------☆☆☆☆☆
nBagSamples = int(len(xTrain) * bagFract)

for iTrees in range(numTreesMax):
    idxBag = []
    for i in range(nBagSamples):
        idxBag.append(random.choice(range(len(xTrain))))
    xTrainBag = [xTrain[i] for i in idxBag]
    yTrainBag = [yTrain[i] for i in idxBag]

    modelList.append(DecisionTreeRegressor(max_depth=treeDepth))
    modelList[-1].fit(xTrainBag, yTrainBag)

    latestPrediction = modelList[-1].predict(xTest)
    predList.append(list(latestPrediction))