shap-model-explainability

To explain model predictions using SHAP values, this Skill computes feature attributions derived from game theory. It quantifies the impact of each feature on model outcomes, demystifying complex algorithms for debugging and fairness analysis.

Yes, you can achieve model interpretability with XGBoost and LightGBM using the Tree explainer. The Skill facilitates explainer selection to compute Shapley values and visualize global feature importance for these tree-based models.

You visualize feature importance for deep learning models using the Deep, Gradient, or Kernel explainers. The Skill computes SHAP values for TensorFlow and Torch models, generating beeswarm plots to show feature impact on predictions.

The best way to calculate feature attributions for individual predictions is by computing SHAP values. This Skill uses Shapley Additive exPlanations to quantify both global and local feature impacts on specific model outcomes.

The appropriate SHAP explainer depends on your ML model: Tree for XGBoost and LightGBM, Deep or Gradient for TensorFlow and Torch, and Kernel or Permutation for general model interpretability and feature attribution computation.

Yes, model interpretability with SHAP works for linear models using the Linear explainer. The Skill computes Shapley values to quantify feature importance and visualize results for fairness analysis and model comparison.