Explain machine learning model predictions using SHAP Shapley values.
npx skills add https://github.com/JosephWoodall/noosphere --skill shap-josephwoodall
Please help me install this Agent Skill. Skill: shap Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/shap Command: npx skills add https://github.com/JosephWoodall/noosphere --skill shap-josephwoodall
SHAP provides principled, locally accurate explanations for any machine learning model by attributing a prediction to input features using Shapley values.
Explain SHAP values for my model on the provided dataset.
High-intent search queries and answers about installing and using this skill.
To explain machine learning model predictions using SHAP values, you compute feature attributions by selecting an appropriate explainer like TreeExplainer, DeepExplainer, or KernelExplainer based on your model type, then pass your model and background data to generate locally accurate feature importance scores.
Feature attributions for ML models can be visualized using beeswarm, waterfall, scatter, heatmap, and force plots to interpret both global feature importance across a dataset and local feature contributions for individual predictions.
SHAP works with tree-based models, deep neural networks, and linear models by providing dedicated explainers—TreeExplainer, DeepExplainer, and LinearExplainer—each optimized for its respective model architecture to compute accurate Shapley values.
SHAP explanations support fairness analysis and model debugging by attributing predictions to input features, allowing you to validate model behavior, detect data leakage, analyze feature interactions, and compare different models within your Python environment.
Handling background data for Shapley value computation requires providing a representative dataset sample to the chosen explainer, which establishes the baseline expectation for feature values and ensures the resulting feature attributions remain locally accurate and principled.
Shapley values provide principled, locally accurate explanations for model predictions by fairly attributing contributions to input features, unlike standard feature importance which often lacks consistency and cannot explain individual predictions accurately.
xiechyCompute SHAP values to attribute predictions to features across model types.
Yezez9Compute SHAP feature attributions and generate explanation plots for machine learning models.
ywh0606Sync delta specification changes into the main specification repository.
liangdabiaoCompute multi-touch attribution using Markov chains and Shapley values.
