EL之Boosting之GB(DTR)：简单回归问题使用梯度提升法(DIY数据集+DTR模型+调两参)

EL之Boosting之GB(DTR)：简单回归问题使用梯度提升法(DIY数据集+DTR模型+调两参)

目录

输出结果

设计思路

核心代码

输出结果

1、eps=0.1,treeDepth=1

2、eps=0.1,treeDepth=5

2、eps=0.3,treeDepth=5

设计思路

核心代码

  

   

    

     

    
    

     

      for iTrees in range(numTreesMax):
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       modelList.append(DecisionTreeRegressor(max_depth=treeDepth))
     
    
   

    

     

    
    

     

       modelList[-1].fit(xTrain, residuals)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       latestInSamplePrediction = modelList[-1].predict(xTrain)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       residuals = [residuals[i] - eps * latestInSamplePrediction[i] for i in range(len(residuals))]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       latestOutSamplePrediction = modelList[-1].predict(xTest)
     
    
   

    

     

    
    

     

       predList.append(list(latestOutSamplePrediction))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      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)]) * eps)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       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))
     
    
  
 

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原文链接：yunyaniu.blog.csdn.net/article/details/86033971