ML之DT：基于简单回归问题训练决策树(DIY数据集+七种{1~7}深度的决策树{依次进行10交叉验证})

ML之DT：基于简单回归问题训练决策树(DIY数据集+七种{1~7}深度的决策树{依次进行10交叉验证})

目录

输出结果

设计思路

核心代码

输出结果

设计思路

核心代码

  

   

    

     

    
    

     

      for iDepth in depthList:
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 for ixval in range(nxval):
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       idxTest = [a for a in range(nrow) if a%nxval == ixval%nxval]
     
    
   

    

     

    
    

     

       idxTrain = [a for a in range(nrow) if a%nxval != ixval%nxval]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       xTrain = [x[r] for r in idxTrain]
     
    
   

    

     

    
    

     

       xTest = [x[r] for r in idxTest]
     
    
   

    

     

    
    

     

       yTrain = [y[r] for r in idxTrain]
     
    
   

    

     

    
    

     

       yTest = [y[r] for r in idxTest]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       treeModel = DecisionTreeRegressor(max_depth=iDepth)
     
    
   

    

     

    
    

     

       treeModel.fit(xTrain, yTrain)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       treePrediction = treeModel.predict(xTest)
     
    
   

    

     

    
    

     

       error = [yTest[r] - treePrediction[r] for r in range(len(yTest))]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 if ixval == 0:
     
    
   

    

     

    
    

     

       oosErrors = sum([e * e for e in error])
     
    
   

    

     

    
    

     
 else:
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       oosErrors += sum([e * e for e in error])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       mse = oosErrors/nrow
     
    
   

    

     

    
    

     

       xvalMSE.append(mse)
     
    
  
 

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原文链接：yunyaniu.blog.csdn.net/article/details/85944969