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Classification tree and regression tree

Decision trees are roughly divided into two types according to the type of objective variable Y. Qualitative data is called a "classification tree" and quantitative data is called a "regression tree".

How to use the regression tree

As the name implies, regression trees can be used to predict the value of Y when any X is entered, similar to regression analysis .

However, in the regression tree, X is basically treated as qualitative data, so the numerical accuracy is not very good. If you try to improve the accuracy of numerical values, it tends to be a complicated model that you do not understand.

I think that causal inference and data mining are more realistic than predictions in using regression trees . Rather than regression analysis, it is an image of innumerable analysis of variance . It is useful as a tool for appropriately grouping and analyzing Y.

Regression tree N-try tree

The regression tree has a binary tree, but it seems that there is no N-ary tree .

If you want to use the goodness of N-shinki , there is a way to cluster Y in one dimension and make it qualitative data.


Examples of R is in the page, Decision tree by R .

NEXT N-try tree