shap

Compute SHAP feature attributions and generate explanation plots for machine learning models.

Updated Mar 10, 2026

Search Tags:#ai #machine learning #feature importance #explainability #shap #model interpretation

Ready to run

npx skills add https://github.com/Yezez9/Research-Agent --skill shap-yezez9

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/shap
Command: npx skills add https://github.com/Yezez9/Research-Agent --skill shap-yezez9

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill demystifies machine learning models by explaining their predictions, identifying key drivers, and revealing how features influence outcomes.

Core Features & Use Cases

Feature Importance: Quantify the impact of each feature on model predictions.
Prediction Explanation: Understand why a model made a specific decision for an individual instance.
Model Debugging: Identify biases, unexpected behaviors, or data leakage.
Use Case: After training a loan approval model, use this Skill to explain why a specific applicant was denied, highlighting the features that contributed most to the decision.

Quick Start

Use the shap skill to generate a beeswarm plot for the model explanations.

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 feature importance?▼

To explain machine learning model predictions, you compute SHAP values to quantify feature attributions and identify key drivers. This approach generates visual plots like waterfalls and beeswarms to reveal how features influence individual predictions.

Can I use SHAP values with XGBoost, LightGBM, TensorFlow, and PyTorch models?▼

Yes, SHAP values work with XGBoost, LightGBM, TensorFlow, and PyTorch models. The Skill leverages these dependencies to calculate feature attributions and provide model interpretability across these different machine learning frameworks.

What is the best way to debug machine learning models for bias and data leakage?▼

The best way to debug machine learning models for bias and data leakage is to analyze SHAP values for unexpected feature attributions. Identifying disproportionately influential features reveals hidden biases and pinpoints data leakage in your workflows.

How do I generate a beeswarm plot for model interpretation?▼

To generate a beeswarm plot for model interpretation, the Skill calculates SHAP values and uses matplotlib for visualizations. This plot visualizes the distribution of feature impacts across your entire dataset to explain complex model behavior.

Why does my loan approval model deny specific applicants and how do I understand feature attributions?▼

To understand why your loan approval model denies specific applicants, SHAP values provide prediction explanations by highlighting the features that contributed most to the decision. This quantifies individual feature attributions for transparent analysis.