ExhauFS: exhaustive search-based feature selection for classification and survival regression.

ABSTRACT: Feature selection is one of the main techniques used to prevent overfitting in machine learning applications. The most straightforward approach for feature selection is an exhaustive search: one can go over all possible feature combinations and pick up the model with the highest accuracy. This method together with its optimizations were actively used in biomedical research, however, publicly available implementation is missing. We present ExhauFS-the user-friendly command-line implementation of the exhaustive search approach for classification and survival regression. Aside from tool description, we included three application examples in the manuscript to comprehensively review the implemented functionality. First, we executed ExhauFS on a toy cervical cancer dataset to illustrate basic co

SUBMITTER: Nersisyan S 

PROVIDER: S-EPMC8976470 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature