Inferring feature importance with uncertainties with application to large genotype data

Estimating feature importance, which is the contribution of a prediction or several predictions due to a feature, is an essential aspect of explaining data-based models. Besides explaining the model itself, an equally relevant question is which features are important in the underlying data generatin...

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Vydané v:	PLoS computational biology Ročník 19; číslo 3; s. e1010963
Hlavní autori:	Johnsen, Pål Vegard, Strümke, Inga, Langaas, Mette, DeWan, Andrew Thomas, Riemer-Sørensen, Signe
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 01.03.2023 Public Library of Science (PLoS)
Predmet:	Artificial intelligence Biobanks Biology and Life Sciences Computer and Information Sciences Confidence intervals Decomposition Engineering and Technology Estimation theory Expected values Game theory Genotype Genotype & phenotype Genotypes Genotyping Techniques Machine learning Neural networks Obesity Physical Sciences Random variables Resampling Research and Analysis Methods Synthetic data Tree structures (Computers) Uncertainty Values Norway United Kingdom > UK
ISSN:	1553-7358, 1553-734X, 1553-7358
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Popis
Shrnutí:	Estimating feature importance, which is the contribution of a prediction or several predictions due to a feature, is an essential aspect of explaining data-based models. Besides explaining the model itself, an equally relevant question is which features are important in the underlying data generating process. We present a Shapley-value-based framework for inferring the importance of individual features, including uncertainty in the estimator. We build upon the recently published model-agnostic feature importance score of SAGE (Shapley additive global importance) and introduce Sub-SAGE. For tree-based models, it has the advantage that it can be estimated without computationally expensive resampling. We argue that for all model types the uncertainties in our Sub-SAGE estimator can be estimated using bootstrapping and demonstrate the approach for tree ensemble methods. The framework is exemplified on synthetic data as well as large genotype data for predicting feature importance with respect to obesity.
Bibliografia:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors declare that they have no competing interests.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1010963