ML之DT:基于简单回归问题训练决策树(DIY数据集+三种深度的二元DT性能比较)
ML之DT:基于简单回归问题训练决策树(DIY数据集+三种深度的二元DT性能比较)
输出结果
设计思路
核心代码
for i in range(1, len(xPlot)):
lhList = list(xPlot[0:i])
rhList = list(xPlot[i:len(xPlot)])
lhAvg = sum(lhList) / len(lhList)
rhAvg = sum(rhList) / len(rhList)
lhSse = sum([(s - lhAvg) * (s - lhAvg) for s in lhList])
rhSse = sum([(s - rhAvg) * (s - rhAvg) for s in rhList])
sse.append(lhSse + rhSse)
xMin.append(max(lhList))
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