Compute SHAP values and generate global and local explanation plots.
npx skills add https://github.com/CRAG666/dotfiles --skill shap-crag666
Please help me install this Agent Skill. Skill: shap Source: https://github.com/CRAG666/dotfiles/tree/main/skills/shap Command: npx skills add https://github.com/CRAG666/dotfiles --skill shap-crag666
This Skill helps you understand and communicate why a machine learning model produced a specific prediction or an overall feature ranking.
Ask to explain “which features drive my model’s prediction” and the Skill will guide you to select an explainer, compute SHAP values, and generate the right plot(s).
High-intent search queries and answers about installing and using this skill.
To explain model predictions, you compute SHAP values to attribute output changes to specific input features. This process requires selecting an appropriate explainer for your model type and generating visualizations like beeswarm or waterfall plots to show feature importance.
Yes, SHAP supports fairness analysis by decomposing model predictions into feature contributions. You can group SHAP values by cohort to inspect for bias, validate model behavior across demographics, and compare attributions to ensure equitable outcomes.
You generate interpretability visualizations by computing SHAP values and mapping them to plots like beeswarm, bar, waterfall, force, scatter, or heatmaps. Local plots explain individual predictions while global plots summarize overall feature importance across the dataset.
Yes, model interpretability via SHAP applies to tree-based models, deep learning, linear models, and black-box models. You must select the corresponding SHAP explainer algorithm designed for your specific model architecture to compute accurate attribution values.
The best way to debug unexpected feature importance is to compute SHAP values to inspect how individual features influence specific predictions. Generating local and global explanation plots helps validate model behavior and identify anomalies in feature attributions.
Ritabrata-ChakrabortyComputes SHAP values to interpret ML model predictions and feature importance.
OwnLabAIExplain ML model predictions by attributing outputs to features using SHAP values.
andikarachmanAttribute model predictions to input features using SHAP values.
tassiovaleExplain machine learning model predictions using SHAP values and plots.
