EL之Bagging：利用Bagging算法實(shí)現(xiàn)回歸預(yù)測(實(shí)數(shù)值評(píng)分預(yù)測)問題

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目錄

輸出結(jié)果

設(shè)計(jì)思路

核心思路

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輸出結(jié)果

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設(shè)計(jì)思路

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核心思路

#4.1、當(dāng)treeDepth=1，對(duì)圖進(jìn)行可視化 #(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(dāng)treeDepth=1，對(duì)圖進(jìn)行可視化 #(1)、定義numTreesMax、treeDepth numTreesMax = 30 treeDepth = 5 # ----------------------▲▲▲▲▲#4.3、當(dāng)treeDepth=12，對(duì)圖進(jìn)行可視化 #(1)、定義numTreesMax、treeDepthnumTreesMax = 100 # ----------------------☆☆☆☆☆ treeDepth = 12 # ----------------------☆☆☆☆☆

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