shap

Feature attribution for machine learning models is computed using SHAP values, which provide principled, additive explanations of how each input influences model outputs. This approach enables both global feature importance and local explanations for debugging and fairness analysis.

Yes, SHAP values can be computed for deep learning and tree-based models using specialized explainers. The framework supports TreeExplainer for tree-based models, DeepExplainer for deep networks, LinearExplainer for linear models, and KernelExplainer for black-box predictors.

Model interpretation results can be visualized using waterfall, beeswarm, bar, scatter, heatmap, and force plots. These visualizations reveal global feature importance and local explanations, showing precise feature contributions for individual predictions across machine learning pipelines.

Yes, background data is required to compute SHAP values for your model. The background data serves as a reference distribution for calculating feature attributions, allowing the explainers to determine how individual features shift predictions away from the baseline expectation.

SHAP should be used for model debugging when you need principled, additive feature attributions that guarantee consistent local and global explanations. Its unified framework supports multiple model architectures and provides interaction values, making it ideal for fairness analysis and model comparison.

Yes, analyzing feature interactions in machine learning models is supported through SHAP interaction values. These values decompose predictions to show not only individual feature contributions but also how pairs of features jointly influence model outputs across different explainer types.