shap-model-explainability

Name: shap-model-explainability
Availability: InStock
Author: jaechang-hits

Compute SHAP feature attributions and visualize model explainability.

298|27|Updated Feb 18, 2026

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

Ready to run

npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill shap-model-explainability

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shap-model-explainability
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/biostatistics/shap-model-explainability
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill shap-model-explainability

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires shap, matplotlib, xgboost, lightgbm, tensorflow, torch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill demystifies complex machine learning models by explaining their predictions and feature importance, making them transparent and trustworthy.

Core Features & Use Cases

Model Interpretability: Understand which features drive predictions using Shapley values.
Feature Importance: Quantify the impact of each feature on model outcomes, both globally and for individual predictions.
Use Case: After training a customer churn prediction model, use this Skill to identify the top 3 factors (e.g., contract duration, monthly charges, customer service calls) that most influence a customer's likelihood to churn.

Quick Start

Explain the SHAP values for the test dataset using the trained XGBoost model and visualize the global feature importance with a beeswarm plot.

Frequently Asked Questions about shap-model-explainability

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

FAQPage Schema

How do I explain machine learning model predictions using SHAP values?▼

To explain model predictions using SHAP values, this Skill computes feature attributions derived from game theory. It quantifies the impact of each feature on model outcomes, demystifying complex algorithms for debugging and fairness analysis.

Can I use SHAP for model interpretability with XGBoost and LightGBM?▼

Yes, you can achieve model interpretability with XGBoost and LightGBM using the Tree explainer. The Skill facilitates explainer selection to compute Shapley values and visualize global feature importance for these tree-based models.

How do I visualize feature importance for a deep learning model?▼

You visualize feature importance for deep learning models using the Deep, Gradient, or Kernel explainers. The Skill computes SHAP values for TensorFlow and Torch models, generating beeswarm plots to show feature impact on predictions.

What is the best way to calculate feature attributions for individual predictions?▼

The best way to calculate feature attributions for individual predictions is by computing SHAP values. This Skill uses Shapley Additive exPlanations to quantify both global and local feature impacts on specific model outcomes.

Which SHAP explainer should I use for different ML models?▼

The appropriate SHAP explainer depends on your ML model: Tree for XGBoost and LightGBM, Deep or Gradient for TensorFlow and Torch, and Kernel or Permutation for general model interpretability and feature attribution computation.

Does model interpretability with SHAP work for linear models?▼

Yes, model interpretability with SHAP works for linear models using the Linear explainer. The Skill computes Shapley values to quantify feature importance and visualize results for fairness analysis and model comparison.