shap

Compute SHAP feature attributions and generate explanation plots for machine learning models.

2|Updated Feb 2, 2026

Search Tags:#debugging #machine learning #model interpretability #feature importance #shap #explainable ai

Ready to run

npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill shap-weiwei-mao

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/Weiwei-Mao/hydrology-skills/tree/main/hydrology-skills/shap
Command: npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill shap-weiwei-mao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides deep insights into how machine learning models make predictions, helping users understand feature importance, debug models, and ensure fairness.

Core Features & Use Cases

Model Interpretability: Understand which features drive predictions using SHAP values.
Feature Importance: Quantify the impact of each feature on model outcomes.
Debugging & Fairness: Identify biases and validate model behavior.
Use Case: After training a loan approval model, use this Skill to explain why a specific applicant was denied, highlighting the key factors that contributed to the decision.

Quick Start

Use the shap skill to generate a beeswarm plot for the model explanations.

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?▼

To explain machine learning model predictions and understand feature importance, you compute SHAP values to quantify the impact of each feature on model outcomes, generating plots like waterfall or beeswarm to visualize the attributions.

Can I use SHAP values to debug model bias and analyze fairness for black-box models?▼

Yes, you can use SHAP values to debug model bias and analyze fairness for black-box models by computing feature attributions to identify which features drive specific predictions and validate model behavior.

Does model interpretability with SHAP work with tree-based models and deep learning?▼

Model interpretability with SHAP works with tree-based models, deep learning, linear models, and black-box models, computing feature attributions to provide deep insights into how complex models make predictions.

What's the best way to visualize feature attributions for explainable AI?▼

The best way to visualize feature attributions for explainable AI is generating various plots such as waterfall, beeswarm, bar, scatter, force, and heatmap to understand complex model behavior and feature impact.

How do I generate a beeswarm plot for model explanations?▼

To generate a beeswarm plot for model explanations, you use the SHAP skill after training your machine learning model to compute feature attributions and visualize the distribution of feature importance across predictions.

Why does my loan approval model deny a specific applicant and what factors contributed?▼

To understand why your loan approval model denies a specific applicant and what factors contributed, you compute SHAP values to highlight the key features that drove the model's decision for that individual prediction.