Explainability

Explainability tools in our platform provide in-depth insights into how individual features drive model predictions, offering both global and local perspectives on feature importance and impact. By leveraging SHAP values, users can analyze feature influence across all predictions to identify high-impact features globally, as well as drill down into local explainability for specific instances. These insights help model owners interpret model behavior, diagnose issues such as drift or performance drops, and understand cohort-specific dynamics that may require tailored model adjustments. With explainability, users can maintain transparency, enhance model trustworthiness, and take proactive steps to refine model performance and fairness.