Evaluating Machine Learning Models

foryourhealth111-pixel/evaluating-machine-learning-models

| Evaluate trained machine learning models with the right metrics and comparison logic. Use for benchmark review, threshold selection, calibration, validation, and model comparison; not for feature engineering or leakage auditing.

4k 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 evaluating-machine-learning-models

What comes with it

15 466 bytes besides the instruction

assets/README.md

assets/visualization_script.py

references/README.md

scripts/README.md

scripts/data_loader.py

scripts/evaluate_model.py

scripts/metrics_calculator.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 Evaluation Suite

Use this skill when the model exists and the question is whether it is good enough.

Overview

This skill focuses on choosing and interpreting the right evaluation metrics for the problem, then comparing candidate models or thresholds.

When to Use This Skill

Comparing candidate models with consistent metrics
Reviewing precision/recall/F1/AUC, regression error, calibration, or ranking quality
Stress-testing validation strategy before deployment or publication

Not For / Boundaries

Building the training pipeline itself: use scikit-learn for classical modeling or ml-pipeline-workflow for end-to-end workflow ownership
Engineering features: use preprocessing-data-with-automated-pipelines
Checking train/test contamination: use ml-data-leakage-guard

Typical Outputs

Metric suite recommendations
Model comparison tables
Notes on threshold tradeoffs, calibration, and validation weaknesses

scikit-learn for class-level error breakdowns and confusion matrices
scientific-reporting when the evaluation must become a deliverable

How to use it

Copy the folder

Take foryourhealth111-pixel/evaluating-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.