shap
Category: data-science-and-ml · License: MIT · Version: 2.1 · Includes scripts
Tags: explainable AI, SHAP, feature attribution, model interpretability, machine learning
When to use it
Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.Example requests
Ask your agent in plain language, for example:- “How do I get SHAP values for my XGBoost classifier and make a beeswarm plot?”
- “I’m working on a LightGBM model predicting 30-day readmission from train.parquet (about 40 features, many correlated like creatinine and eGFR). I ran shap.TreeExplainer(model).shap_values(X_test) and got a list of two arrays, then plotting with values[1] fails with a shape error on the newer shap version. Also I get an additivity check error when I switch to model_output=‘probability’. How do I fix this?”
- “I trained a gradient boosted model on patient lab data and a reviewer asked us to show why the model flagged certain patients as high risk and which inputs drive predictions overall. How should I go about producing that for the paper, including making sure the explanations are trustworthy?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.