generating-shap-explanations

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.