What is the importance of the train/test split in model development?

Example Answer

I would keep training, model selection and final evaluation separate. Fit weights and learned preprocessing on the training data, use validation data or cross-validation to choose architecture and hyperparameters, and evaluate the final choice on an untouched test set. In vision, correlated frames or images of the same subject can leak across a random split, so the split should reflect the intended deployment. Repeatedly tuning against test results turns that test set into another validation set.

Make it your own

Illustrative split manifest: group by patient, subject, video or capture session as appropriate; record split IDs and prevent augmented versions of one image crossing splits.

Why this works

This preserves the final test as evidence of generalization. A high score on leaked neighboring frames can be misleading. Follow-up: when would a time-based holdout be stronger than a random image split?

Sources: scikit-learn model evaluation.