shap

Explain model predictions by attributing feature contributions with SHAP values.

1|Updated Mar 4, 2026

Search Tags:#visualization #explainability #shap #explainers #model-interpretation #feature-attribution

Ready to run

npx skills add https://github.com/Hung-3008/agusta --skill shap-hung-3008

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/shap
Command: npx skills add https://github.com/Hung-3008/agusta --skill shap-hung-3008

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SHAP provides principled, local- and global-level explanations for any machine learning model by attributing predictions to input features via Shapley values.

Core Features & Use Cases

Global feature importance: summarize how features drive predictions across the dataset.
Local explanations: decompose individual predictions to reveal each feature's contribution.
Interaction insights: quantify and visualize feature interactions and model behavior across models.

Quick Start

Load your trained model and data, create the appropriate SHAP explainer (TreeExplainer for tree models, DeepExplainer for neural nets, LinearExplainer for linear models, or KernelExplainer for black-box cases), compute SHAP values on a representative background, then visualize global and local explanations.

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?▼

Explain model predictions by attributing each feature's contribution using SHAP values. You compute Shapley values on a representative background dataset to decompose individual predictions and quantify global feature importance across classification and regression tasks.

Can I use SHAP explainers for both tree-based models and deep learning?▼

Yes, SHAP explainers support tree-based models, deep learning, linear, and black-box models. You select the appropriate explainer—TreeExplainer, DeepExplainer, LinearExplainer, or KernelExplainer—to compute feature attributions based on your specific model architecture.

What is the difference between local explanations and global feature importance?▼

Local explanations decompose individual predictions to reveal each feature's contribution for a single instance, while global feature importance summarizes how features drive predictions across the entire dataset using aggregated SHAP values.

How do I get started with visualizing model interpretability results?▼

Load your trained model and data, create the appropriate SHAP explainer, compute SHAP values on a representative background, then use visualization tools to present global and local explanations for your model predictions.

When should I use KernelExplainer instead of TreeExplainer?▼

Use KernelExplainer for black-box models where you lack internal model architecture access, and TreeExplainer specifically for tree-based models to compute exact SHAP values efficiently.

Do I need a background dataset to compute SHAP values?▼

Yes, you must construct an appropriate background dataset to compute SHAP values. This representative data serves as the reference distribution for attributing feature contributions to your model's predictions.