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shap

Explain machine learning model predictions using SHAP values and plots.

Updated Jun 30, 2026
Search Tags:#machine learning#model interpretability#feature importance#explainable AI#SHAP
One-click install
npx skills add https://github.com/SunS1eep1ng/codex-dotfiles --skill shap-suns1eep1ng
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/SunS1eep1ng/codex-dotfiles/tree/main/skills/shap
Command: npx skills add https://github.com/SunS1eep1ng/codex-dotfiles --skill shap-suns1eep1ng

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires shap, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides model interpretability and explainability using SHAP (SHapley Additive exPlanations), helping users understand machine learning model predictions, compute feature importance, and generate SHAP plots.

Core Features & Use Cases

  • Model Interpretability: Explain machine learning model predictions and understand the impact of features.
  • Feature Importance: Compute feature importance for any model type.
  • SHAP Plots: Generate SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap) for visual analysis.
  • Use Case: When you need to debug models, analyze model bias or fairness, compare models, or implement explainable AI.

Quick Start

Use the shap skill to explain the prediction of a model for a given input.

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?▼

Model predictions are explained using SHAP values derived from cooperative game theory to quantify each feature's impact. This Skill applies cooperative game theory to compute feature importance and generate visual explanations for black-box model predictions.

Can I use SHAP to compute feature importance for XGBoost and deep learning models?▼

Yes, SHAP computes feature importance for tree-based models like XGBoost, LightGBM, and Random Forest, as well as deep learning models built with TensorFlow and PyTorch. It also supports linear models and general black-box model architectures.

What types of SHAP plots can I generate for model interpretability analysis?▼

You can generate waterfall, beeswarm, bar, scatter, force, and heatmap plots for visual model interpretability analysis. These SHAP visualizations help debug models, analyze bias, and compare model behavior across different feature distributions.

When should I use SHAP values for explainable AI instead of other interpretability methods?▼

Use SHAP values for explainable AI when you need consistent feature importance attribution across different model types. Unlike simpler methods, SHAP provides both global interpretability and local explanation accuracy by grounding feature impact in cooperative game theory.

Do I need the shap library installed to generate model interpretability visualizations?▼

Yes, the shap library is required to compute SHAP values and generate interpretability visualizations. This Skill depends on the shap package to perform the underlying mathematical calculations and render the model explanation plots.

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