shap

You can explain machine learning model predictions by computing SHAP values to decompose predictions into individual feature contributions. This skill guides you through selecting an appropriate explainer, providing background data for baselines, computing the values, and visualizing the results.

Global explanations show overall feature importance across a dataset, while local explanations detail feature contributions for a specific prediction. SHAP values provide a principled way to generate both, enabling transparent debugging and actionable fairness analysis.

Yes, you can explain predictions for deep neural networks and tree-based models. This skill supports multiple explainers including TreeExplainer, DeepExplainer, KernelExplainer, LinearExplainer, GradientExplainer, and PermutationExplainer across different model architectures.

To compute SHAP values for a trained model, you must select the appropriate explainer for your model type and provide background data for baselines. The skill guides you through applying the explainer to generate and visualize the feature attributions.

You need to provide background data when calculating SHAP values to establish a baseline for feature attribution. This baseline allows the explainer to accurately measure how individual features contribute to the difference between the current prediction and the average prediction.

Use TreeExplainer for tree-based models to get exact SHAP values efficiently, whereas KernelExplainer applies to any differentiable model using approximations. Selecting the appropriate explainer ensures accurate feature attribution across debugging, feature engineering, and fairness analysis.