Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.

ABSTRACT: The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows

SUBMITTER: Goodwin NL 

PROVIDER: S-EPMC11268425 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature