Conditional permutation importance revisited

Background Random forest based variable importance measures have become popular tools for assessing the contributions of the predictor variables in a fitted random forest. In this article we reconsider a frequently used variable importance measure, the Conditional Permutation Importance (CPI). We ar...

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Vydáno v:BMC bioinformatics Ročník 21; číslo 1; s. 1 - 30
Hlavní autoři: Debeer, Dries, Strobl, Carolin
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 14.07.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Accuracy
Algorithms
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer simulation
Conditional permutation importance
Life Sciences
Machine Learning and Artificial Intelligence in Bioinformatics
Methodology
Methodology Article
Microarrays
Open source software
Permutations
Pharmaceutical industry
Random forest
Random variables
Regression analysis
Source code
Conditional permutation importance
R
Random forest
ISSN:1471-2105, 1471-2105
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Shrnutí:Background Random forest based variable importance measures have become popular tools for assessing the contributions of the predictor variables in a fitted random forest. In this article we reconsider a frequently used variable importance measure, the Conditional Permutation Importance (CPI). We argue and illustrate that the CPI corresponds to a more partial quantification of variable importance and suggest several improvements in its methodology and implementation that enhance its practical value. In addition, we introduce the threshold value in the CPI algorithm as a parameter that can make the CPI more partial or more marginal. Results By means of extensive simulations, where the original version of the CPI is used as the reference, we examine the impact of the proposed methodological improvements. The simulation results show how the improved CPI methodology increases the interpretability and stability of the computations. In addition, the newly proposed implementation decreases the computation times drastically and is more widely applicable. The improved CPI algorithm is made freely available as an add-on package to the open-source software R. Conclusion The proposed methodology and implementation of the CPI is computationally faster and leads to more stable results. It has a beneficial impact on practical research by making random forest analyses more interpretable.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-03622-2