EL之Bagging：利用Bagging算法实现回归预测(实数值评分预测)问题

EL之Bagging：利用Bagging算法实现回归预测(实数值评分预测)问题

目录

输出结果

设计思路

核心思路

输出结果

设计思路

核心思路

      #4.1、当treeDepth=1，对图进行可视化
      #(1)、定义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、当treeDepth=1，对图进行可视化
      #(1)、定义numTreesMax、treeDepth
      numTreesMax = 30
      treeDepth = 5 # ----------------------▲▲▲▲▲
      #4.3、当treeDepth=12，对图进行可视化
      #(1)、定义numTreesMax、treeDepth
      numTreesMax = 100 # ----------------------☆☆☆☆☆
      treeDepth = 12 # ----------------------☆☆☆☆☆
  
 

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原文链接：yunyaniu.blog.csdn.net/article/details/85994379