You have trained and shipped a machine learning model, and the team wants confidence that its performance will hold up outside the initial offline results. You need a clear evaluation process that catches overfitting, unstable thresholds, and score quality issues before the model affects users.
How do you ensure that your machine learning models are robust and reliable?
Validation stability across foldsProbability calibration qualityThreshold-dependent precision and recall tradeoffsConfusion matrix costs by business outcome