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.

Data, AI & Research|v1|Updated 7/2/2026|GitHub source

MCP get_skill({ skillId: "evaluating-machine-learning-models-371e8939" })

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# 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

## Related Skills

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

#broad-capability#creative#llm#evaluation