Exploitation of surrogate variables in random forests for unbiased analysis of mutual impact and importance of features.

ABSTRACT:

Motivation

Random forest is a popular machine learning approach for the analysis of high-dimensional data because it is flexible and provides variable importance measures for the selection of relevant features. However, the complex relationships between the features are usually not considered for the selection and thus also neglected for the characterization of the analysed samples.

Results

Here we propose two novel approaches that focus on the mutual impact of features in random forests. Mutual forest impact (MFI) is a relation parameter that evaluates the mutual association of the features to the outcome and, hence, goes beyond the analysis of correlation coefficients. Mutual impurity reduction (MIR) is an importance measure that combines this relation parameter with the i

SUBMITTER: Voges LF 

PROVIDER: S-EPMC10403431 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature