Variable importance measures for random forests

Title

Variable importance measures for random forests

 Author

Boon, Cindy (TU Delft Electrical Engineering, Mathematics and Computer Science)

Contributor

Parolya, N. (mentor) ORCID 0000-0003-2147-2288
Ferreira, José (graduation committee)
Kurowicka, D. (graduation committee) ORCID 0000-0002-2897-3862

 Degree granting institution

Delft University of Technology

 Programme

Applied Mathematics

 Date

2021-07-09

 Abstract

Measuring variable importance is often a difficult task: among others models can be complex and covariates can interact with each other and can be correlated. This study focuses on two questions: First, what should be the theoretical measure of variable importance under a given data-generating model? And second, what are the best estimates of these theoretical measures? Two theoretical measures and some corresponding estimates are presented of which one is the well-known random forests variable importance measure (Breiman, 2001). A simulation study is done for both linear and nonlinear models to find out what are the best estimates of variable importance measures for given data-generating models. Most measures struggle when covariates are correlated, but make an improvement in performance when the number of split variables is tuned.

 Subject

Random Forests
Variable importance measures
Correlation

 To reference this document use:

http://resolver.tudelft.nl/uuid:bc1b0369-efea-47e6-95e1-ece779ce736a

 Part of collection

Student theses

 Document type

master thesis

 Rights

© 2021 Cindy Boon
Files
PDF Master_thesis_report_CJM_Boon.pdf 1.69 MB
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