Loading content
Systematic assessment of machine learning models using metrics, validation techniques and error analysis to decide on deployment readiness.
Model evaluation is a systematic process for assessing machine learning models using appropriate metrics, validation strategies, and error analysis. It covers test sets, cross-validation, calibration and fairness checks to determine performance, robustness and readiness for deployment. Emphasis is on reproducible measurements and monitoring readiness.
Share of correctly predicted examples; suitable for balanced classes.
Important for class imbalance; shows trade-off between false positives and negatives.
Measures deviation between predicted probability and observed frequency.
Evaluation process using precision/recall curves, ROC and calibration for production release.
Regular re-evaluation of ranking metrics and alignment with user feedback.
Systematic check for biases across demographic groups with documented mitigation.
Define business-relevant metrics and acceptance criteria.
Perform reproducible evaluation runs (cross-validation, hold-out).
Create baselines, documentation and integrate monitoring metrics into CI/CD.