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