Compute SHAP values and generate feature attribution visualizations for any model type.
npx skills add https://github.com/galeep/plugin-place --skill shap-galeep
Please help me install this Agent Skill. Skill: shap Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-machine-learning/skills/shap Command: npx skills add https://github.com/galeep/plugin-place --skill shap-galeep
This Skill provides model interpretability and explainability using SHAP (SHapley Additive exPlanations), helping users understand machine learning model predictions, compute feature importance, and create visualizations for debugging and analysis.
Compute SHAP values for the model's prediction on a single instance using the SHAP explainer.
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
High-intent search queries and answers about installing and using this skill.
You explain model predictions by computing SHAP values to quantify each feature's contribution to a specific prediction, applying this technique to any model type for model interpretability and explainability.
Feature importance visualizations include waterfall, beeswarm, bar, scatter, force, and heatmap plots to visualize model behavior and debug predictions using SHAP values.
Yes, SHAP applies to any model type to provide feature attribution and explain predictions, requiring the shap library for computation and visualization of feature contributions.
Compute feature attribution for a single instance by initializing a SHAP explainer like TreeExplainer with your model, then passing the instance to calculate its SHAP values.
You need SHAP for model interpretability when you want to understand feature importance, explain a model's prediction, or debug model behavior through feature contribution analysis.
SHAP requires the shap library for computation and visualization, focusing on quantifying feature contributions and generating plots, which may have computational costs depending on model complexity and dataset size.
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