Explain model predictions with SHAP values and visualizations.
npx skills add https://github.com/SciMate-AI/scicli --skill shap-scimate-ai
Please help me install this Agent Skill. Skill: shap Source: https://github.com/SciMate-AI/scicli/tree/main/internal/skills/bundled/claude-scientific-skills/skills/shap Command: npx skills add https://github.com/SciMate-AI/scicli --skill shap-scimate-ai
SHAP provides a principled approach to attribute a model's prediction to its input features, enabling both global and local explanations.
Run a SHAP explanation on your trained model by selecting an appropriate explainer (TreeExplainer for tree models, DeepExplainer for neural networks, etc.) and compute SHAP values for your dataset.
High-intent search queries and answers about installing and using this skill.
To explain model predictions using SHAP values, you select an appropriate explainer class like TreeExplainer or DeepExplainer for your model type, then compute SHAP values for your dataset to generate numerical attributions and visualizations for local and global feature importance.
Global feature importance in SHAP uses summary plots to show overall model behavior across a dataset, while local explanations provide detailed feature attributions for individual predictions to understand specific model outputs.
Yes, SHAP works with both tree-based models and deep learning neural networks by providing specific explainer classes, namely TreeExplainer for tree models and DeepExplainer for neural networks, to compute accurate feature attributions.
You analyze feature interactions in structured data using SHAP by computing SHAP values with an appropriate explainer like KernelExplainer or LinearExplainer, which reveals interaction effects and helps debug model behavior and fairness.
You should use KernelExplainer instead of TreeExplainer when explaining non-tree models or complex structured data where specific explainers do not apply, as it provides model-agnostic feature attribution using SHAP values.
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