Does it work with black-box models?

Absolutely. While specialized explainers are faster, the skill provides instructions for using KernelExplainer, which is model-agnostic and works with any predictive function.

How does this help with model fairness?

The skill includes specific workflows for comparing SHAP values across different cohorts or demographic groups, allowing you to detect and analyze potential bias in model predictions.

What models does the SHAP skill support?

It supports a wide range of models including tree-based models (XGBoost, LightGBM, CatBoost), deep learning (TensorFlow, PyTorch), linear models, and black-box models via KernelExplainer.

Can I use this skill to generate visualizations?

Yes, the skill provides guidance for creating global importance plots (beeswarm, bar), individual prediction breakdowns (waterfall, force), and feature relationship plots (scatter, heatmap).

Is it efficient for large datasets?

Yes, the skill includes optimization strategies such as using specialized explainers (TreeExplainer), data sampling, and batching to maintain performance on large datasets.

SHAP Model Explainability

Name: SHAP Model Explainability
Author: jimmc414

byjimmc414

•

324

Data Science & ML

Explains machine learning model predictions and feature importance using Shapley Additive exPlanations for transparent and interpretable AI.

About

This skill integrates SHAP (SHapley Additive exPlanations) into Claude Code, providing a robust toolkit for model interpretability and explainable AI (XAI). It enables users to compute SHAP values for a wide variety of model architectures—including tree-based models like XGBoost, deep learning frameworks like PyTorch and TensorFlow, and linear regressions. By generating essential visualizations such as beeswarm, waterfall, and force plots, the skill helps developers debug model behavior, identify data leakage, and ensure fairness by quantifying the specific contribution of every feature to individual predictions.

Key Features

Production-ready deployment strategies for real-time model explanation services
Model debugging patterns to identify misclassifications and unexpected feature interactions
Fairness and bias analysis workflows using demographic cohort comparison
Comprehensive visualization suite including beeswarm, waterfall, bar, and scatter plots
Automated selection of optimized SHAP explainers for tree, linear, and deep learning models
324 GitHub stars

Use Cases

Generating feature importance summaries to explain model logic to non-technical stakeholders
Identifying and mitigating algorithmic bias through protected attribute analysis
Debugging individual model errors by visualizing the contribution of each input feature

About

Key Features

Production-ready deployment strategies for real-time model explanation services
Model debugging patterns to identify misclassifications and unexpected feature interactions
Fairness and bias analysis workflows using demographic cohort comparison
Comprehensive visualization suite including beeswarm, waterfall, bar, and scatter plots
Automated selection of optimized SHAP explainers for tree, linear, and deep learning models
324 GitHub stars

Use Cases

Generating feature importance summaries to explain model logic to non-technical stakeholders
Identifying and mitigating algorithmic bias through protected attribute analysis
Debugging individual model errors by visualizing the contribution of each input feature