Why Split Data

Splitting data into separate sets for training and evaluation is fundamental to building models that generalize. Without held-out data, there is no reliable way to know whether a model has learned meaningful patterns or simply memorized its training examples.

Data splitting provides an honest estimate of how a model will perform on new, unseen inputs in production.