Abess

Not to be confused with Abbess or Leonard L. Abess.

abess (Adaptive Best Subset Selection, also ABESS) is a machine learning method designed to address the problem of best subset selection. It aims to determine which features or variables are crucial for optimal model performance when provided with a dataset and a prediction task. abess was introduced by Zhu in 2020 [1] and it dynamically selects the appropriate model size adaptively, eliminating the need for selecting regularization parameters.

abess is applicable in various statistical and machine learning tasks, including linear regression, the Single-index model, and other common predictive models.[1][2] abess can also be applied in biostatistics.[3][4][5][6]

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