Compute SHAP feature attributions and baseline deviations for machine learning predictions.
npx skills add https://github.com/gabrielvuksani/wotann --skill shap-gabrielvuksani
Please help me install this Agent Skill. Skill: shap Source: https://github.com/gabrielvuksani/wotann/tree/main/skills/scientific/shap Command: npx skills add https://github.com/gabrielvuksani/wotann --skill shap-gabrielvuksani
SHAP helps you understand why a machine learning model produced a particular prediction by attributing the output to individual input features.
Use SHAP to explain which features most influenced a prediction by running a SHAP explainer, computing SHAP values for your dataset, and producing a beeswarm plot for global importance.
High-intent search queries and answers about installing and using this skill.
Feature importance is computed using SHAP values to quantify each feature's contribution to a prediction relative to a baseline. This explains why a machine learning model produced a particular output by attributing it to individual input features.
You can analyze tree-based models, deep learning models, linear models, and black-box predictors for model interpretability. Selecting the appropriate SHAP explainer allows you to compute feature attributions across these different model architectures.
Yes, you can visualize feature attributions using SHAP plotting workflows that generate beeswarm, bar, waterfall, scatter, force, and heatmap plots. These visualizations provide both global feature importance understanding and per-instance local explanations.
Use SHAP values for fairness analysis and debugging by computing feature attributions to diagnose unexpected model behavior and investigate errors. Analyze potential bias by examining how individual input features influence predictions across different demographic groups.
Start with explainable AI for black-box predictors by selecting a model-agnostic SHAP explainer, computing SHAP values for your dataset, and generating a beeswarm plot. This workflow reveals global feature importance and individual prediction contributions for opaque models.
schneidermuCompute SHAP values and generate global and local model explanations.
Yezez9Compute SHAP feature attributions and generate explanation plots for machine learning models.
K-Dense-AICompute SHAP values to attribute model predictions to input features.
rocklambrosGenerate SHAP attributions for trained machine learning models.
