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shap

Compute Shapley values to explain model predictions and feature importance.

2|Updated Jun 4, 2026
Search Tags:#machine learning#feature importance#model debugging#explainability#shap#ai fairness
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill shap-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/shap
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill shap-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires shap, numpy, pandas, scikit-learn, matplotlib, scipy, and includes references (resource) components.

What problem does it solve?

This skill addresses the black-box nature of machine learning models by providing a unified framework to explain predictions, identify feature importance, and debug model behavior.

Core Features & Use Cases

  • Model Explainability: Compute SHAP values to understand exactly which features drive individual predictions or global model behavior.
  • Visualization Suite: Generate publication-quality plots including waterfall, beeswarm, bar, and scatter plots to communicate insights effectively.
  • Use Case: Use this skill to diagnose why a model is misclassifying specific samples or to validate that a model is not relying on biased features during a fairness audit.

Quick Start

Use the shap skill to compute and visualize the feature importance for my trained xgboost model using the test dataset.

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 and quantify feature importance?▼

To explain machine learning predictions and quantify feature importance, compute Shapley values to identify exactly which features drive individual predictions or global model behavior across diverse workflows.

Can I compute Shapley values for deep learning and tree-based models using scikit-learn?▼

Yes, you can compute Shapley values for deep learning, linear, and tree-based models. The framework requires integration with scikit-learn, xgboost, or deep learning frameworks to perform attribution analysis.

What is the best way to debug why a machine learning model is misclassifying specific samples?▼

The best way to debug misclassifications is to compute Shapley values to diagnose why a model predicts specific outcomes, validating whether it relies on biased features during a fairness audit.

How do I generate visualizations for model explainability and feature attribution?▼

To generate visualizations for model explainability, create publication-quality plots including waterfall, beeswarm, bar, and scatter plots using matplotlib to effectively communicate feature attribution insights.

Does model interpretability with Shapley values work for black-box models?▼

Yes, Shapley values support black-box models by providing a unified framework to explain predictions and identify feature importance, addressing the black-box nature of machine learning models.

What are the limitations of using Shapley values for model debugging?▼

Computing Shapley values requires integration with scikit-learn, xgboost, or deep learning frameworks, and depends on numpy, pandas, scipy, and matplotlib to perform attribution analysis and generate diagnostic visualizations.

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