When you train a model, you would like your model to generalize and perform well on new, unseen data. You don’t want your model to overfit to training data. A popular way to have an unbiased approach to measuring how well your model generalizes is to split your training data into train, validation, and test sets. They are sets because examples should be unique to each set, otherwise you may have leakage.
The type of split you use should depend on the nature or distribution of the training data.
If you have a static dataset or a dataset drawn from a stationary distribution, a popular way to split your training data is to create random splits. Typical sizes for the different sets would be 70% for your train set, 15% for the validation set, and 15% for the test set.
The splitting process should ensure that the distribution of data in each of the train/validation/test sets are representative of the overall dataset. If, however, some examples are at risk of being under-represented in one of your sets (for example, because you have only a few examples for a certain category in a categorical variable) then you can use stratified sampling, In stratified sampling, the data is divided in such a way that the proportion of each class or category in the test set is the same as in the original dataset, ensuring a balanced representation of different classes or categories.
For time-series data, however, you typically create the train/validation sets from non-overlapping time ranges of data. The test set should be drawn from a time range after the train set (to evaluate if the model can generalize to work well in future unseen data).
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