Attribute model predictions to individual features using SHAP explainers.
npx skills add https://github.com/fuzzy-dynamics/strings --skill shap-fuzzy-dynamics
Please help me install this Agent Skill. Skill: shap Source: https://github.com/fuzzy-dynamics/strings/tree/main/packages/skills/shap Command: npx skills add https://github.com/fuzzy-dynamics/strings --skill shap-fuzzy-dynamics
SHAP provides principled, local and global attributions for any ML model, enabling transparent understanding of predictions and trust in AI decisions.
Train a model, choose the appropriate SHAP explainer (TreeExplainer for tree-based models, DeepExplainer for neural networks), compute SHAP values on a test set, and generate a beeswarm plot to view global feature importance.
High-intent search queries and answers about installing and using this skill.
SHAP explains model predictions by attributing outputs to individual features. It provides principled, local and global attributions for any ML model, enabling transparent understanding of predictions and trust in AI decisions.
You interpret them using TreeExplainer for tree-based models and DeepExplainer for neural networks. These explainers compute SHAP values to attribute outputs to individual features, generating visualizations like beeswarm and waterfall plots for debugging.
Yes, linear models can be interpreted using LinearExplainer or model-agnostic variants like KernelExplainer. These compute feature attributions without needing direct model internals, enabling both local explanations and global feature importance.
Global feature importance is best visualized using beeswarm, bar, or heatmap plots. These visualizations aggregate local SHAP values across a test set to reveal feature importance and interactions for model comparison and fairness analysis.
Yes, SHAP supports background data handling to compute accurate feature attributions. This enables fair model comparison and auditing by providing consistent local explanations and global feature importance across datasets.
Use KernelExplainer when interpreting models without specific explainers, like ensembles or custom algorithms. Use TreeExplainer specifically for tree-based models to achieve faster, exact feature attribution computations.
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