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