Model Evaluation, Generalization, And Sampling Guide
A model can look excellent during training and still fail in real life. That is why evaluation, generalization, and sampling are core machine learning skills. The practical question is not “Did the model memorize the training data?” It is “Will this model work on new data?” For practical ML, evaluation is the difference between a model that looks good in an experiment and a model that can be trusted in a real workflow. ...