shap

Generate SHAP values and visualizations to explain machine learning model predictions.

Updated Mar 15, 2026

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

Ready to run

npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill shap-sagunkayastha

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/sagunkayastha/claude_skills_collection/tree/main/machine-learning-ai/shap
Command: npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill shap-sagunkayastha

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires shap, matplotlib, numpy, pandas, scikit-learn, 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, making them transparent and understandable.

Core Features & Use Cases

Feature Importance: Identify which features most influence model outcomes.
Prediction Explanation: Understand why a specific prediction was made.
Model Debugging: Detect bias, fairness issues, or unexpected model behavior.
Use Case: You've built a credit risk model. Use this Skill to explain why a particular applicant was denied, highlighting the key factors that led to the decision.

Quick Start

Use the shap skill to generate a beeswarm plot for the SHAP values of 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 feature importance in machine learning model predictions?▼

Explain machine learning model predictions by calculating SHAP values to quantify feature contributions. This Skill computes SHapley Additive exPlanations to identify which features most influence specific outcomes and overall model behavior.

Can I use SHAP values to explain both XGBoost and deep learning models?▼

Yes, SHAP values can explain both XGBoost and deep learning models. This Skill supports model interpretability across tree-based models using LightGBM and XGBoost, as well as deep learning frameworks like TensorFlow and PyTorch.

What visualizations help understand why a specific prediction was made?▼

Visualizations like waterfall, beeswarm, and scatter plots help understand why a specific prediction was made. This Skill generates these diverse plots to provide both local explanations for individual predictions and global explanations for model behavior.

How do I detect bias and fairness issues in a credit risk model?▼

Detect bias and fairness issues in a credit risk model by analyzing SHAP values to debug unexpected model behavior. This Skill highlights key factors leading to decisions, allowing you to identify feature contributions that may indicate bias.

Does model interpretability work with linear models and scikit-learn?▼

Yes, model interpretability works with linear models and scikit-learn. This Skill supports calculating SHAP values for various model types, including linear models, tree-based algorithms, and deep learning networks.

What is the best way to generate a beeswarm plot for SHAP values?▼

The best way to generate a beeswarm plot for SHAP values is to use this Skill's built-in scripts. It processes your test dataset to produce visualizations that map feature importance and model interpretability across all predictions.