Comparison of feature importance measures as explanations for classification models

• Published in: SN applied sciences Vol. 3; no. 2; p. 272
• Main Authors: Saarela, Mirka, Jauhiainen, Susanne
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2021; Springer Nature B.V
• Subjects: Applied and Technical Physics; Archives & records; Artificial intelligence; Breast cancer; Chemistry/Food Science; Classification; Datasets; Earth Sciences; Engineering; Engineering: Smart Information and Communication Technologies; Environment; Explainable artificial intelligence; Feature selection; Machine learning; Materials Science; Medical research; Regression analysis; Research Article; Explainable artificial intelligence; Feature importance; Logistic regression; Random forest; Interpretable models

Explainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature importance. However, there are several different approaches how feature impor...