Can a Transparent Machine Learning Algorithm Predict Better than Its Black Box Counterparts? A Benchmarking Study Using 110 Data Sets

We developed a novel machine learning (ML) algorithm with the goal of producing transparent models (i.e., understandable by humans) while also flexibly accounting for nonlinearity and interactions. Our method is based on ranked sparsity, and it allows for flexibility and user control in varying the...

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Bibliographic Details

Published in:	Entropy
Main Authors:	Ryan A. Peterson, Max McGrath, Joseph E. Cavanaugh
Format:	Article
Language:	English
Published:	MDPI AG 2024-08-01
Subjects:	model selection feature selection lasso explainable machine learning
Online Access:	https://www.mdpi.com/1099-4300/26/9/746