1. 程式人生 > >EL之Bagging(DTR):利用Bagging對迴歸問題(實數值評分預測)建模(調2參)

EL之Bagging(DTR):利用Bagging對迴歸問題(實數值評分預測)建模(調2參)

EL之Bagging(DTR):利用Bagging對迴歸問題(實數值評分預測)建模(調2參)

輸出結果

 

設計思路

 

核心程式碼

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))