shap

Attribute model predictions to input features using SHAP values.

2|Updated Apr 12, 2026

Search Tags:#visualization #machine-learning #explainability #interpretability #shap #feature-attribution

Ready to run

npx skills add https://github.com/Clay-HHK/claude-config --skill shap-clay-hhk

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/shap
Command: npx skills add https://github.com/Clay-HHK/claude-config --skill shap-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SHAP provides a principled approach to attribute model predictions to individual input features, enabling transparent interpretation of complex models.

Core Features & Use Cases

Global and local explanations for tree-based, deep learning, and linear models
Interactive and static visualizations to inspect feature impact and relationships
Debugging, fairness analysis, model comparisons, and guidance for feature engineering

Quick Start

Explain a trained model's predictions by computing SHAP values and visualizing the results.

Frequently Asked Questions about shap

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I explain machine-learning model predictions using SHAP values?▼

To explain machine-learning model predictions, SHAP attributes outputs to input features by applying specific explainers for tree-based, deep learning, and linear models to compute feature impact.

What is feature attribution and how does it help with model debugging?▼

Feature attribution computes individual feature impacts on model predictions, enabling transparent model debugging and bias analysis by highlighting how specific inputs drive outputs across different model architectures.

Can I use SHAP explainers for both global and local interpretability?▼

Yes, SHAP explainers support both global and local interpretability for tree-based, deep learning, and linear models, allowing you to inspect overall feature impact and individual prediction attributions.

Do I need to prepare background data for SHAP feature attribution?▼

Yes, computing SHAP feature attributions requires handling background data correctly and specifying the proper model-output configuration to ensure accurate explanations across different model architectures.

What's the best way to visualize feature impact for machine-learning models?▼

The best way to visualize feature impact is by generating interactive and static visualizations from computed SHAP values, which inspect relationships and feature attributions for debugging and model comparison.