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shap

Compute SHAP values and visualize model predictions with Shapley-based plots.

1|Updated Sep 30, 2025
Search Tags:#visualizations#machine-learning#explainability#shap#explainable-ai#feature-importance#model-interpretability
One-click install
npx skills add https://github.com/AI4EFin/AdaptiveWIN-SHAP --skill shap-ai4efin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/AI4EFin/AdaptiveWIN-SHAP/tree/main/.claude/skills/shap
Command: npx skills add https://github.com/AI4EFin/AdaptiveWIN-SHAP --skill shap-ai4efin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SHAP provides a principled, scalable way to explain model predictions by attributing output to individual features using Shapley values. It makes machine learning decisions more transparent, trustworthy, and actionable.

Core Features & Use Cases

  • Global & local explanations: Compute SHAP values for any model and visualize with waterfall, beeswarm, bar, and scatter plots.
  • Model debugging & fairness: Debug model behavior, check for bias, and compare models.
  • Production-ready explainability: Integrate explanations into dashboards or APIs to support decision-making.

Quick Start

Train a tree-based model (e.g., XGBoost), create a TreeExplainer, compute SHAP values on your test data, and visualize a global importance plot such as a beeswarm.