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shap

Generate SHAP-based feature importance and prediction explanations for machine learning models.

1|Updated Jan 14, 2026
Search Tags:#machine learning#xai#model interpretability#feature importance#shap#explainable ai
One-click install
npx skills add https://github.com/Sologa/codex-pipeline --skill shap-sologa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/shap
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill shap-sologa

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill demystifies machine learning models by explaining their predictions and identifying key drivers, making complex models understandable and trustworthy.

Core Features & Use Cases

  • Feature Importance: Determine which features most influence model predictions.
  • Prediction Explanations: Understand why a specific prediction was made for an individual instance.
  • Model Debugging: Identify potential biases, data leakage, or unexpected model behavior.
  • Use Case: A data scientist can use this Skill to explain why a loan application was denied by showing which applicant features (e.g., credit score, income) contributed most to the negative prediction.

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 identify feature importance?▼

To explain machine learning model predictions and identify feature importance, you calculate SHAP values to quantify each feature's contribution to individual predictions and overall model behavior, providing both global and local model interpretability.

What is the best way to visualize SHAP values for model interpretability?▼

The best way to visualize SHAP values for model interpretability is by generating diverse plots like waterfall, beeswarm, bar, and scatter plots, which effectively illustrate feature importance and explain specific predictions.

Does SHAP work with deep learning models and tree-based models like XGBoost?▼

Yes, SHAP works with deep learning models and tree-based models like XGBoost. It supports model interpretability across various architectures including tree-based, deep learning, and linear models for comprehensive bias analysis.

Can I use SHAP for model debugging to identify potential biases and data leakage?▼

Yes, you can use SHAP for model debugging to identify potential biases, data leakage, or unexpected model behavior. By analyzing SHAP values, you can detect which features drive anomalies in your machine learning predictions.

How do I generate a local explanation for a specific prediction instance?▼

To generate a local explanation for a specific prediction instance, you calculate its SHAP values and use waterfall plots. This shows exactly which features, such as credit score or income, contributed most to that individual prediction.

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