Explain model predictions by attributing outcomes to input features with SHAP values.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill shap-jasrajtulsi
Please help me install this Agent Skill. Skill: shap Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/shap Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill shap-jasrajtulsi
This Skill helps you understand why a machine learning model made a prediction by turning feature contributions into clear, defensible explanations.
Use the shap skill to explain your model’s predictions, identify the most important features, and generate the most suitable SHAP plots for both global and individual interpretation.
High-intent search queries and answers about installing and using this skill.
Model prediction explanation breaks down outcomes into baseline shifts and per-feature attributions using SHAP values. This process clarifies how individual input features drive a specific prediction, enabling clear and defensible interpretation.
SHAP explainers support deep learning networks, tree-based models, linear models, and black-box estimators. Appropriate explainer selection is required to accurately attribute prediction outcomes to input features across these different model architectures.
Global interpretation summarizes feature importance across many samples using SHAP-based rankings and distribution views. Suitable plotting workflows include beeswarm, bar, and heatmap visualizations to reveal overall feature effects and model behavior.
Model debugging and validation spot leakage, unexpected feature effects, and instability by analyzing SHAP attributions. Comparing local and global feature contributions helps identify misleading behavior and validate trained models effectively.
Fairness and comparison analysis assess bias and consistency by comparing SHAP explanations across cohorts or multiple models. This highlights differential feature effects and ensures prediction behavior remains consistent across different demographic groups.
Local prediction explanation utilizes waterfall, force, scatter, and decision plots to visualize per-feature attributions. These plotting workflows illustrate how individual features shift predictions from the baseline to the final output.
AlterLab-IEUExplain model predictions with SHAP values and attributions across model types.
dralkhCompute SHAP values and generate feature attribution plots for machine learning models.
aliyunGenerate SHAP-based explainability reports for trained XGBoost classification models.
tondevrelAnalyze scikit-learn model feature importance with SHAP, LIME, and partial dependence plots.
