EL之Bagging：利用Bagging演算法實現迴歸預測(實數值評分預測)問題

輸出結果

設計思路

核心思路

#4.1、當treeDepth=1，對圖進行視覺化
#(1)、定義numTreesMax、treeDepth
numTreesMax = 30
treeDepth = 1                           # ----------------------▲▲▲▲▲



modelList = []
predList = []


#number of samples to draw for stochastic bagging
nBagSamples = int(len(xTrain) * 0.5)


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))

mse = []
allPredictions = []
for iModels in range(len(modelList)):
    prediction = []
    for iPred in range(len(xTest)):
        prediction.append(sum([predList[i][iPred] for i in range(iModels + 1)])/(iModels + 1))

    allPredictions.append(prediction)
    errors = [(yTest[i] - prediction[i]) for i in range(len(yTest))]
    mse.append(sum([e * e for e in errors]) / len(yTest))


#4.2、當treeDepth=1，對圖進行視覺化
#(1)、定義numTreesMax、treeDepth
numTreesMax = 30
treeDepth = 5                           # ----------------------▲▲▲▲▲




#4.3、當treeDepth=12，對圖進行視覺化
#(1)、定義numTreesMax、treeDepth

numTreesMax = 100                       # ----------------------☆☆☆☆☆
treeDepth = 12                          # ----------------------☆☆☆☆☆