迴歸樹與基於規則的模型(part3)--迴歸模型樹

學習筆記，僅供參考，有錯必糾

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迴歸樹與基於規則的模型

迴歸模型樹

One limitation of simple regression trees is that each terminal node(最終節點) uses the average of the training set outcomes(訓練結果變量的平均值) in that node for prediction. As a consequence, these models may not do a good job predicting samples whose true outcomes are extremely high or low.

One approach to dealing with this issue is to use a different estimator(其他隊的估計量) in the terminal nodes.

Here we focus on the model tree approach described in Quinlan (1992) called M5, which is similar to regression trees except:

• 切分的準則不同；
• 最終節點利用線性模型來對結果變量進行預測(而不是使用簡單的平均)；
• 新樣本的預測值通常是樹中同一條路徑下，若干不同模型預測值的組合。

Like simple regression trees, the initial split(初次切分) is found using an