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AI & Machine Learning · Foundational

Train-Test Split

Dividing a dataset into separate subsets for training and evaluation.

Definition

A common split is 70-80% training with the rest for validation and testing. Splits should be random and stratified for classification, but time-ordered for time-series data to avoid using future information. Group-aware splits prevent the same entity appearing in both sets.

Example

Splitting 10,000 records into 8,000 for training, 1,000 for validation and 1,000 for testing.

Exam tip

For time-series, split by time, never randomly.

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