shap

Model prediction explanations use feature attribution to decompose outputs into individual feature contributions. This Skill computes SHAP values to transparently justify AI decisions by attributing predictions to specific input features.

SHAP values can be computed for deep learning and tree-based models. This Skill applies TreeExplainer, DeepExplainer, GradientExplainer, LinearExplainer, and KernelExplainer to support regression and classification tasks across these architectures.

Global feature importance and local predictions are understood through SHAP visualizations. This Skill generates beeswarm and bar plots for overall model drivers, alongside waterfall and force plots to debug individual predictions.

A representative background dataset is required to perform model interpretation with SHAP. This dataset serves as the reference distribution needed by the explainer classes to accurately compute feature attribution values for your trained models.

Feature interactions and non-linear effects are analyzed using SHAP dependence analysis. This Skill uncovers relationships between features, allowing you to visualize how individual variables interact and impact model predictions.

SHAP explanations support production workflows including fairness analysis and model comparison. This Skill provides feature attribution insights needed to check for bias, compare model behaviors, and ensure transparent deployment considerations.