shap

You can explain machine learning model predictions by computing SHAP feature attributions to quantify how each feature contributes to a single prediction locally and overall behavior globally, then visualize the results using standardized plots.

Yes, you can use SHAP to check fairness and detect bias by investigating errors and analyzing feature attributions across different cohorts, which helps identify patterns of bias in your model's behavior.

SHAP works with tree, linear, deep learning, and black-box models by selecting an appropriate SHAP explainer for your specific model type and computing the corresponding feature attributions.

The best way to visualize feature attributions for model interpretability is by generating standardized SHAP plots such as beeswarm, bar, waterfall, scatter, force, and heatmap to validate model logic and communicate results.

To debug why a model predicted a specific case, you ask the skill to explain the prediction, compute the SHAP values with correct output units, and produce the top contributing features visualized in a waterfall plot.