Can I use this for fairness and bias analysis?

Yes, the skill includes specific workflows to identify protected attributes, compare feature importance across demographic groups, and detect proxy features that may introduce bias.

Which machine learning models are supported by the SHAP skill?

It supports tree-based models like XGBoost and LightGBM, deep learning frameworks like TensorFlow and PyTorch, linear models, and any black-box model using KernelExplainer.

Which SHAP explainer should I use for my specific model?

The skill guides you through the choice: TreeExplainer for trees, DeepExplainer for neural networks, LinearExplainer for linear models, or KernelExplainer for general black-box functions.

How does this skill help with model debugging?

It provides structured workflows to explain misclassified samples, identify unexpected feature importance that might indicate data leakage, and validate that feature relationships make sense.

Does this skill help with production deployment of explanations?

Yes, it provides a dedicated workflow for production deployment, covering explainer serialization, API integration, and performance optimization for real-time explanations.

SHAP Model Explainability

Name: SHAP Model Explainability
Author: Weiwei-Mao

byWeiwei-Mao

0•

Ciencia de Datos y ML

Explains machine learning model predictions and feature importance using SHAP values and comprehensive visualizations.

This skill enables Claude to provide deep insights into machine learning models by implementing SHAP (SHapley Additive exPlanations). It guides users through selecting the right explainer for various model types—including tree-based, deep learning, and linear models—while generating essential visualizations like beeswarm, waterfall, and scatter plots. It is particularly useful for debugging model behavior, analyzing feature interactions, ensuring fairness, and implementing explainable AI (XAI) in production environments to make complex models transparent and trustworthy.

Características Principales

01Automated selection of optimal SHAP explainers for Tree, Deep, Linear, and Black-box models

020 GitHub stars

03Comprehensive guidance for calculating feature contributions and complex feature interactions

04Advanced workflows for model debugging, bias detection, and demographic fairness analysis

05Generation of global and local visualizations including beeswarm, waterfall, bar, and force plots

06Performance optimization strategies for large datasets and production-ready XAI deployments

Casos de Uso

01Detecting and mitigating data leakage or bias in machine learning pipelines

02Identifying which specific features drive individual predictions for model transparency

03Comparing feature importance across different model architectures to select the most interpretable version

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add weiwei-mao/hydrology-skills shap

For use in Claude.ai and ChatGPT

Download Skill