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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Veröffentlicht in:PLoS computational biology Jg. 19; H. 3; S. e1010963
Hauptverfasser: Johnsen, Pål Vegard, Strümke, Inga, Langaas, Mette, DeWan, Andrew Thomas, Riemer-Sørensen, Signe
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.03.2023
Public Library of Science (PLoS)
Schlagworte:
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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Zusammenfassung: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.
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The authors declare that they have no competing interests.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1010963