shap

Compute SHAP feature importance and generate model explanation plots.

8|Updated Nov 19, 2025

Search Tags:#machine learning #xai #model interpretability #feature importance #shap #explainable ai

Ready to run

npx skills add https://github.com/sanand0/scientific-research --skill shap-sanand0

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/shap
Command: npx skills add https://github.com/sanand0/scientific-research --skill shap-sanand0

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill demystifies machine learning models by explaining their predictions and identifying key drivers, making complex AI understandable and trustworthy.

Core Features & Use Cases

Feature Importance: Determine which features most influence model outcomes.
Prediction Explanation: Understand why a model made a specific prediction for an individual instance.
Model Debugging: Identify biases, validate behavior, and pinpoint areas for improvement.
Use Case: A credit scoring model flags an applicant as high-risk. Use this Skill to explain which factors (e.g., debt-to-income ratio, credit history length) contributed most to that decision.

Quick Start

Use the shap skill to generate a beeswarm plot for the provided 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 using SHAP values?▼

You can explain machine learning model predictions by computing SHAP values to quantify each feature's contribution. This skill generates visual plots like waterfall and beeswarm to show exactly how features drive individual outcomes and overall model behavior.

Can I use SHAP for model interpretability with XGBoost and LightGBM?▼

Yes, SHAP supports model interpretability for XGBoost and LightGBM. It calculates Shapley Additive exPlanations across various model architectures including tree-based models, deep learning frameworks, and linear models to identify feature importance.

What is the best way to debug machine learning models and analyze bias?▼

The best way to debug machine learning models and analyze bias is by computing SHAP values to validate behavior and identify key drivers. This process pinpoints areas for improvement by revealing how specific features influence risk outcomes.

Does SHAP work with TensorFlow and PyTorch deep learning models?▼

SHAP works with TensorFlow and PyTorch deep learning models to provide explainable AI. It computes feature importance and generates visual explanations for complex neural network predictions across different model architectures.

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

To generate a beeswarm plot for model explanations, use this skill to compute SHAP values and visualize feature importance distributions. It creates scatter plots that aggregate individual feature impacts across your entire dataset.

Why should I use SHAP values instead of basic feature importance?▼

You should use SHAP values instead of basic feature importance because they provide both global and local model interpretability. They explain individual predictions using Shapley Additive exPlanations, revealing exactly how much each feature contributes to a specific outcome.