JSAI2020

Presentation information

Interactive Session

[4Rin1] Interactive 2

Fri. Jun 12, 2020 9:00 AM - 10:40 AM Room R01 (jsai2020online-2-33)

[4Rin1-26] Variable Importance Measures Based on Occurrence Ratio for Tree Ensembles

〇Kota Mata1, Kentaro Kanamori1, Hiroki Arimura1 (1.Hokkaido University)

Keywords:tree ensembles, feature importance, interaction term

As one of the methods for interpreting the prediction result of a complex machine learning model, variable importance scores of a tree ensemble are widely used. However, existing variable importance methods measure how much a single variable has contributed to the output. Therefore, when a relationship between two or more variables (such as XOR) contributes to the output, their importance scores are often not accurately evaluated. In this paper, we propose a new variable importance score based on the occurrence ratio of a variable in a tree ensemble. By experiments, we confirm the effectiveness of our proposed method in comparison with existing methods.

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