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?” Quick Answer Evaluate machine learning models by separating training, validation, and test data, comparing metrics across those splits, checking for overfitting, and using repeatable sampling methods so experiments can be reproduced. ...