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shap

Explain machine learning model predictions using SHAP values and plots.

Updated Feb 26, 2026
Search Tags:#machine learning#model interpretability#feature importance#SHAP#explanable AI
One-click install
npx skills add https://github.com/gracefullight/iqc --skill shap-gracefullight
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/gracefullight/iqc/tree/main/.agents/skills/shap
Command: npx skills add https://github.com/gracefullight/iqc --skill shap-gracefullight

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides comprehensive model interpretability and explainability using SHAP (SHapley Additive exPlanations), enabling users to understand machine learning model predictions, compute feature importance, and generate various SHAP plots.

Core Features & Use Cases

  • Feature Importance: Compute SHAP values for any model type to understand the impact of features on model predictions.
  • SHAP Plots: Generate SHAP plots like waterfall, beeswarm, bar, scatter, force, and heatmap to visualize model behavior.
  • Model Debugging: Use SHAP to debug and validate model behavior, analyze model bias or fairness, and compare models.
  • Use Case: If you are working with tree-based models (XGBoost, LightGBM, Random Forest) or deep learning models (TensorFlow, PyTorch), this Skill helps you to interpret and explain model predictions.

Quick Start

Use the shap skill to explain the predictions of a trained XGBoost model on a 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 using SHAP values?▼

You can explain machine learning model predictions by computing SHAP values to understand the impact of features on predictions for tree-based models, deep learning models, linear models, and black-box models. This Skill generates the necessary values for model interpretability.

Can I generate SHAP plots like waterfall and beeswarm for an XGBoost model?▼

Yes, you can generate SHAP plots including waterfall, beeswarm, bar, scatter, force, and heatmap visualizations to analyze model behavior. This Skill supports plotting for tree-based models like XGBoost, LightGBM, CatBoost, and Random Forest.

Does model interpretability with SHAP work with TensorFlow and PyTorch?▼

Model interpretability with SHAP works with deep learning models built using TensorFlow, PyTorch, and Keras. The Skill computes feature importance and generates explanations for these deep learning model architectures.

What is the best way to debug machine learning model bias and feature importance?▼

The best way to debug machine learning model bias and feature importance is using SHAP to validate model behavior and compare models. This Skill helps analyze fairness by visualizing exactly how features impact model predictions.

Do I need a specific library to compute SHAP values for feature importance?▼

Yes, you need the SHAP library installed to compute SHAP values and generate visualizations. This Skill requires the SHAP library as a dependency to perform model interpretability and feature importance calculations.

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