You've trained a machine learning model and offline results look promising. Before putting it into production, your team wants a clear validation process that checks whether the model is reliable and fit for real use.
How do you validate a machine learning model before deployment?
Cross-validation for generalization checksPrecision and recall tradeoffs at the operating thresholdConfusion matrix interpretation for business impactCalibration of predicted probabilities before deployment