You've trained and shipped a machine learning model, and the team wants confidence that its offline performance will hold up when used in practice. You need a clear evaluation approach that catches overfitting, unstable thresholds, and score quality issues before they affect users or downstream decisions.
How do you ensure that your machine learning models are robust and reliable?
Stable performance across folds and time splitsMinimal gap between validation and holdout resultsWell-calibrated probabilitiesThresholds aligned to business trade-offsClear error patterns from confusion matrix review