輸出結果
ML之DT：基于簡單回歸問題訓練決策樹(DIY資料集+七種{1~7}深度的決策樹{依次進行10交叉驗證})
設計思路
ML之DT：基于簡單回歸問題訓練決策樹(DIY資料集+七種{1~7}深度的決策樹{依次進行10交叉驗證})
核心代碼
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)