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Interpreting Models via Single Tree Approximation · Around

2016

Interpreting Models via Single Tree Approximation

Zhou, Yichen, Hooker, Giles

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We propose a procedure to build a decision tree which approximates the performance of complex machine learning models.

This single approximation tree can be used to interpret and simplify the predicting pattern of random forests (RFs) and other models.
The use of a tree structure is particularly relevant in medical questionnaires where it enables an adaptive shortening of the questionnaire, reducing response burden.
We study the asymptotic behavior of splits and introduce an improved splitting method designed to stabilize tree structure.

Built on

A multiple comparison procedure for comparing several treatments with a control
Charles W Dunnett · 1955
Earlier work this paper cites.
Classification and regression trees
Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen · 1984
Earlier work this paper cites.
Kernel smoothing
Original
Matt P Wand and M Chris Jones · 1994
Earlier work this paper cites.
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini and Yosef Hochberg · 1995
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