shap

Compute SHAP values and generate SHAP plots for machine learning model predictions.

3|Updated Apr 17, 2026

Search Tags:#model interpretability #feature importance #explainable AI #model debugging #SHAP

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill shap-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/shap
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill shap-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides model interpretability and explainability using SHAP, allowing users to understand and communicate the reasons behind machine learning model predictions.

Core Features & Use Cases

SHAP Values: Compute SHAP values for any model type to understand feature contributions.
Visualizations: Generate SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap) to visualize feature importance and model behavior.
Debugging: Debug and validate model behavior, analyze model bias or fairness, and compare models.
Use Case: A data scientist uses this Skill to explain a machine learning model's prediction for a specific instance, identifying which features are most influential.

Quick Start

Use the shap skill to explain the prediction of a trained XGBoost model on a given test instance.

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?▼

Explain machine learning model predictions using SHAP values by computing feature contributions for specific instances. This Skill calculates SHapley Additive exPlanations to identify which features most influence a model's output, helping you understand and communicate prediction reasoning.

Can I generate feature importance plots for tree-based models like XGBoost and LightGBM?▼

Yes, you can generate feature importance plots for tree-based models like XGBoost, LightGBM, and Random Forest. This Skill creates visualizations including waterfall, beeswarm, bar, scatter, force, and heatmap plots to visualize model behavior and feature importance.

Does SHAP work with deep learning frameworks like TensorFlow and PyTorch?▼

SHAP works with deep learning frameworks like TensorFlow and PyTorch, as well as linear and black-box models. You need compatible model training code and the shap library installed to compute feature contributions across these different model architectures.

What is the best way to debug model bias and validate model behavior?▼

The best way to debug model bias and validate behavior is by analyzing SHAP values and their associated plots. This Skill helps you compare models, analyze fairness, and validate that your machine learning model is making predictions for the right reasons.

Do I need compatible model training code to compute SHAP explanations?▼

Yes, you need compatible model training code to compute SHAP explanations. This Skill requires a trained machine learning model and the shap library dependency to process your model and generate the associated interpretability plots for your predictions.