Compute SHAP values to quantify feature contributions in machine learning predictions.
npx skills add https://github.com/nferrer-dev/claude-dotfiles --skill shap-nferrer-dev
Please help me install this Agent Skill. Skill: shap Source: https://github.com/nferrer-dev/claude-dotfiles/tree/main/skills/shap Command: npx skills add https://github.com/nferrer-dev/claude-dotfiles --skill shap-nferrer-dev
This skill addresses the black-box nature of machine learning models by providing a unified, mathematically grounded framework to explain individual predictions and global feature importance.
Trigger the shap skill to compute and visualize feature importance for your trained model using the test dataset.
High-intent search queries and answers about installing and using this skill.
Shapley values explain machine learning model predictions by quantifying each feature's contribution to individual predictions using cooperative game theory. This framework supports model-agnostic and model-specific workflows to generate both global and local explanations.
Yes, you can compute feature importance for tree-based models, deep learning frameworks, linear models, and black-box models. The framework requires integration with scikit-learn, xgboost, or deep learning frameworks to calculate Shapley values for these model types.
The best way to visualize feature contributions for model interpretability is by generating waterfall, beeswarm, bar, scatter, and force plots. These visualizations help illustrate how individual features impact model predictions across local and global contexts.
Model interpretability helps with debugging and fairness analysis by identifying data leakage, analyzing model bias, and validating feature relationships. Explaining predictions ensures model reliability and fairness by revealing how features drive outcomes.
Yes, you need scikit-learn and pandas along with numpy, scipy, and matplotlib to generate SHAP explanations. These dependencies support the data manipulation, model integration, and visualization suite required to compute and plot Shapley values.
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