Evaluate trained machine learning models using metrics suited to the task. Compare candidates consistently, review decision thresholds and probability estimates, and identify weaknesses in validation before deployment or publication.
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.
Review the instructions the agent will receive before you install.
Use this skill when the model exists and the question is whether it is good enough.
This skill focuses on choosing and interpreting the right evaluation metrics for the problem, then comparing candidate models or thresholds.
scikit-learn for classical modeling or ml-pipeline-workflow for end-to-end workflow ownershippreprocessing-data-with-automated-pipelinesml-data-leakage-guardscikit-learn for class-level error breakdowns and confusion matricesscientific-reporting when the evaluation must become a deliverableNo reviews yet. Tell others what you thought.
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