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shap

Explain machine learning model predictions using SHAP values and plots.

13|3|Updated Jun 10, 2026
Search Tags:#machine learning#model interpretability#feature importance#SHAP#explanable AI
One-click install
npx skills add https://github.com/tassiovale/claude-code-kit --skill shap-tassiovale
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/shap
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill shap-tassiovale

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 and communicate model predictions, compute feature importance, and visualize model behavior.

Core Features & Use Cases

  • Model Interpretability: Explain machine learning model predictions and understand the impact of features on predictions.
  • Feature Importance: Compute SHAP values for any model type to determine the importance of each feature.
  • Visualization: Generate SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap) to visualize feature importance and model behavior.
  • Use Case: Use this Skill when analyzing model predictions, debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI.

Quick Start

Use the shap skill to explain the predictions of a trained XGBoost model on the test set 'X_test.csv'.

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 understand feature importance?▼

You can explain machine learning model predictions by computing SHAP values, which quantify the contribution of each feature to a specific prediction, enabling in-depth interpretability and feature importance analysis.

Can I use SHAP to interpret predictions from XGBoost, LightGBM, and deep learning models?▼

Yes, SHAP supports interpreting predictions from tree-based models like XGBoost, LightGBM, and Random Forest, as well as deep learning models built with TensorFlow and PyTorch, plus linear and black-box models.

How do I generate visualizations to debug model bias and analyze model behavior?▼

You can generate visualizations to debug model bias and analyze behavior by creating SHAP plots such as waterfall, beeswarm, bar, scatter, force, and heatmap charts to visually inspect feature impacts on predictions.

What is the best way to compare models and implement explainable AI for black-box algorithms?▼

The best way to compare models and implement explainable AI for black-box algorithms is to compute SHAP values across different models, allowing you to consistently evaluate and communicate how features drive individual predictions.

Do I need the shap library to compute feature importance for my trained models?▼

Yes, you need the shap library installed as a dependency to compute SHAP values, calculate feature importance, and generate the visual plots required for model interpretability and explainability analysis.

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