|
ИСТИНА |
Войти в систему Регистрация |
ФНКЦ РР |
||
ExhauFS: exhaustive search-based feature selection for classification and survival regressionстатья
- Авторы: Nersisyan S., Novosad V., Galatenko A., Sokolov Andrey, Bokov G., Konovalov A., Alekseev D., Tonevitsky A.
- Журнал: PeerJ
- Том: 10
- Номер: e13200
- Год издания: 2022
- Издательство: PeerJ
- Местоположение издательства: San Francisco
- DOI: 10.7717/peerj.13200
- Аннотация: 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.
- Добавил в систему: Алексеев Дмитрий Владимирович