shap

Attribute model predictions to individual features using SHAP explainers.

1|2|Updated Apr 29, 2026

Search Tags:#visualization #explainability #shap #model-interpretation #feature-attribution #tree-explainer #kernel-explainer

Ready to run

npx skills add https://github.com/fuzzy-dynamics/strings --skill shap-fuzzy-dynamics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/fuzzy-dynamics/strings/tree/main/packages/skills/shap
Command: npx skills add https://github.com/fuzzy-dynamics/strings --skill shap-fuzzy-dynamics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SHAP provides principled, local and global attributions for any ML model, enabling transparent understanding of predictions and trust in AI decisions.

Core Features & Use Cases

SHAP explainers for trees, deep nets, linear models, and model-agnostic variants to compute feature attributions.
Global and local visualizations (beeswarm, bar, waterfall, scatter, heatmap) to reveal feature importance and interactions.
Use cases include debugging model behavior, auditing fairness, comparing models, and deploying explanations in production.

Quick Start

Train a model, choose the appropriate SHAP explainer (TreeExplainer for tree-based models, DeepExplainer for neural networks), compute SHAP values on a test set, and generate a beeswarm plot to view global feature importance.

Frequently Asked Questions about shap

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I explain model predictions using feature attribution?▼

SHAP explains model predictions by attributing outputs to individual features. It provides principled, local and global attributions for any ML model, enabling transparent understanding of predictions and trust in AI decisions.

How do I interpret tree-based models and neural networks?▼

You interpret them using TreeExplainer for tree-based models and DeepExplainer for neural networks. These explainers compute SHAP values to attribute outputs to individual features, generating visualizations like beeswarm and waterfall plots for debugging.

Can I use model-agnostic explainers for linear models?▼

Yes, linear models can be interpreted using LinearExplainer or model-agnostic variants like KernelExplainer. These compute feature attributions without needing direct model internals, enabling both local explanations and global feature importance.

What is the best way to visualize global feature importance?▼

Global feature importance is best visualized using beeswarm, bar, or heatmap plots. These visualizations aggregate local SHAP values across a test set to reveal feature importance and interactions for model comparison and fairness analysis.

Does SHAP support background data handling for model comparison?▼

Yes, SHAP supports background data handling to compute accurate feature attributions. This enables fair model comparison and auditing by providing consistent local explanations and global feature importance across datasets.

When should I use model-agnostic KernelExplainer instead of TreeExplainer?▼

Use KernelExplainer when interpreting models without specific explainers, like ensembles or custom algorithms. Use TreeExplainer specifically for tree-based models to achieve faster, exact feature attribution computations.