Explain model predictions by attributing outputs to input features using SHAP.
npx skills add https://github.com/Org-GAgent/result-interpreter --skill shap-org-gagent
Please help me install this Agent Skill. Skill: shap Source: https://github.com/Org-GAgent/result-interpreter/tree/main/.skills/scientific-skills/shap Command: npx skills add https://github.com/Org-GAgent/result-interpreter --skill shap-org-gagent
SHAP provides principled, local and global explanations for any machine learning model by attributing predictions to input features, enabling transparency and trust.
Start by training or loading your model, then create an appropriate SHAP explainer (TreeExplainer for trees, DeepExplainer for neural nets, LinearExplainer for linear models), compute SHAP values on your dataset, and visualize results.
High-intent search queries and answers about installing and using this skill.
Model interpretability explains predictions by attributing outputs to input features using SHAP values. It generates global feature importance and local explanations for individual predictions across tree-based, deep learning, and linear models.
Global feature importance and local explanations for tree-based models are best generated using TreeExplainer. It computes SHAP values to produce beeswarm, bar, waterfall, and force plots that visualize feature attribution.
Yes, SHAP explainers support neural networks via DeepExplainer and linear models via LinearExplainer. KernelExplainer provides model-agnostic feature attribution for other architectures.
Correct feature attribution requires interpreting model outputs as raw scores, probabilities, or log-odds. Selecting appropriate background data and feature names ensures accurate SHAP value computation.
Feature attribution supports interaction detection, model debugging, and fairness analysis. SHAP values identify how input features interact and influence predictions, enabling transparency and trust across different model types.
ManfronEnricoCompute SHAP values and visualizations for tree, deep, and linear models.
SologaGenerate SHAP-based feature importance and prediction explanations for machine learning models.
AI4EFinCompute SHAP values and visualize model predictions with Shapley-based plots.
swaruplabExplain model predictions with SHAP values and feature contribution attributions.
