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})

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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