shap

Generates SHAP values to explain feature importance and model predictions.

557|98|Updated Nov 7, 2025

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

Ready to run

npx skills add https://github.com/jimmc414/Kosmos --skill shap-jimmc414

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/shap
Command: npx skills add https://github.com/jimmc414/Kosmos --skill shap-jimmc414

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires shap, matplotlib, numpy, pandas, scikit-learn, xgboost, lightgbm, tensorflow, torch, and includes references (resource) components.

What problem does it solve?

This Skill demystifies complex machine learning models by explaining their predictions, identifying key drivers, and revealing feature importance, making AI more transparent and trustworthy.

Core Features & Use Cases

Model Interpretability: Understand why a model makes specific predictions using Shapley values.
Feature Importance: Quantify the impact of each feature on model outcomes, both globally and for individual predictions.
Debugging & Validation: Identify model biases, data leakage, and unexpected behaviors.
Use Case: A data scientist can use this Skill to explain a credit risk model, identifying which factors (e.g., credit score, income, loan amount) most strongly influence a loan approval or denial 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 using Shapley values?▼

You can explain machine learning model predictions using Shapley values to quantify each feature's contribution to specific outcomes. This approach demystifies complex models by identifying key drivers and revealing global and local feature importance.

Can I use SHAP for model interpretability with deep learning frameworks like TensorFlow and PyTorch?▼

Yes, SHAP supports model interpretability for deep learning frameworks like TensorFlow and PyTorch. It also works with tree-based models like XGBoost and LightGBM, as well as linear models, to compute feature importance across diverse architectures.

What's the best way to visualize feature importance for model debugging?▼

The best way to visualize feature importance for model debugging is by generating SHAP beeswarm plots. These plots help identify model biases, data leakage, and unexpected behaviors by showing the distribution of feature impacts across predictions.

Does SHAP work with scikit-learn models for fairness analysis?▼

Yes, SHAP works with scikit-learn models for fairness analysis. It quantifies the impact of each feature on model outcomes, allowing you to identify biases and validate that predictions are driven by appropriate factors rather than unexpected behaviors.

Why do I need the shap library to perform explainable AI analysis?▼

You need the shap library to perform explainable AI analysis because it provides the necessary computational and visualization functions for SHapley Additive exPlanations. It requires dependencies like matplotlib, numpy, and pandas to process data and render plots.