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generating-shap-explanations

Generate SHAP attributions for trained machine learning models.

2|Updated May 23, 2026
Search Tags:#shap#deep-learning#model-interpretability#feature-attribution#xgboost#background-dataset#stability-check
One-click install
npx skills add https://github.com/rocklambros/rcs --skill generating-shap-explanations
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: generating-shap-explanations
Source: https://github.com/rocklambros/rcs/tree/main/skills/ml-datasci/generating-shap-explanations
Command: npx skills add https://github.com/rocklambros/rcs --skill generating-shap-explanations

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill turns opaque model outputs into defensible feature-attribution explanations, helping you answer why a model predicted a result and which inputs matter most.

Core Features & Use Cases

  • Local explanations: Generates per-instance SHAP waterfall views for a specific prediction so you can trace which features pushed the output up or down.
  • Global interpretability: Produces mean absolute SHAP rankings and beeswarm summaries to identify the most important features across many cases.
  • Robust workflow controls: Chooses the right explainer for tree, deep, kernel, or black-box models, requires a deliberate background dataset, and checks attribution stability across resamples.
  • Use cases: Model debugging, stakeholder-facing explanations, regulatory reporting, and analyzing surprising predictions in clinical, finance, or ML pipelines.

Quick Start

Ask the skill to explain one model prediction and the overall feature importance for the same trained model, using a stratified background sample and a stability check.

Frequently Asked Questions about generating-shap-explanations

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

FAQPage Schema
How do I generate SHAP explanations for a trained XGBoost or deep learning model?▼

You can generate SHAP explanations by selecting the appropriate explainer for your tree, deep, or black-box model, then using a deliberately chosen background dataset to produce per-instance and global feature attribution summaries.

What is the best way to check SHAP attribution stability across resamples?▼

To check SHAP attribution stability, you validate the attributions across background resamples to ensure your feature importance rankings remain consistent and defensible rather than artifacts of a specific sample.

Can I get both local and global feature attribution for black-box pipelines?▼

Yes, you can extract local per-instance SHAP waterfall views to trace individual predictions and global mean absolute SHAP rankings to identify the most important features across black-box pipelines.

Does SHAP model interpretability require a specific background dataset?▼

Yes, generating stable SHAP model interpretability requires a deliberately chosen, stratified background dataset to accurately establish the baseline for feature attribution comparisons.

Why do my SHAP feature importance rankings change for the same model?▼

Unstable SHAP feature importance rankings often occur when the background dataset is not deliberately stratified, making background-stability validation across resamples essential for reliable explanations.

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