Feature importance, contribution and dependency analysis.

Feature importance, contribution and dependency analysis.

<p>A: the 20 most important features. B: summary plot for feature value contribution. The x-axis represents the SHAP values, representing the impact that feature had on the model’s performance. C–H: SHAP dependence plots. These plots show the effect that a single feature has on the model and t...

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Bibliographic Details
Main Author: Chao Wang (146527) (author)
Other Authors: Quan Zou (157931) (author)
Published: 2024
Subjects:
Biochemistry
Microbiology
Genetics
Ecology
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
various signal transduction
valuable bioinformatics tool
interaction exploration indicating
comparison results shown
benefiting practical experiments
based genetic algorithm
art available toolkits
effective feature subset
phosphorylation site prediction
div >< p
feature contribution
protein phosphorylation
phosphorylation modifications
source code
several state
proposed model
predicting task
offspring competition
model organisms
fungal species
functional understanding
distributed information
cellular processes
balanced performance
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