Explaining Machine Learning Models

foryourhealth111-pixel/explaining-machine-learning-models

| Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.

5k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

2583

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/foryourhealth111-pixel/Vibe-Skills --skill explaining-machine-learning-models

What comes with it

20 069 bytes besides the instruction

assets/README.md

assets/example_explanation.json

assets/explanation_template.html

assets/visualization_styles.css

references/README.md

scripts/README.md

scripts/data_preprocessing.py

scripts/explain_model.py

scripts/feature_importance.py

What it tells the agent to use

found in the instruction text

Bash runs shell commands — read the instruction before connecting

The instruction itself

6 sections, as written by the author

Model Explainability Tool

Positioning

Treat this skill as an explicit/manual helper for interpretability work.

When to Use

Use this skill when:

Understand why a machine learning model made a specific prediction.
Identify the most important features influencing a model's output.
Debug model performance issues by identifying unexpected feature interactions.
Communicate model insights to non-technical stakeholders.
Ensure fairness and transparency in model predictions.

Not For / Boundaries

Model training and hyperparameter search: use scikit-learn
Benchmark comparison and threshold selection: use evaluating-machine-learning-models
Leakage or prediction-time audits: use ml-data-leakage-guard

Typical Outputs

Feature importance or attribution summaries
Local explanation workflow for a concrete prediction
Notes on caveats, instability, or misleading explanations

shap for SHAP-specific workflows
evaluating-machine-learning-models when the question is whether the model is good enough

How to use it

Copy the folder

Take foryourhealth111-pixel/explaining-machine-learning-models from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.