Train-Test Split.

Dividing data into separate sets for training and evaluating model performance.

In-depth explanation

The training set is used to train the model; the test set evaluates final performance on unseen data. A validation set (from training data) is used for hyperparameter tuning. Typical splits are 80/20 or 70/15/15. Proper splitting prevents data leakage and gives honest performance estimates. Time-series data requires temporal splits.

Examples

EX. 01

80% train, 20% test

EX. 02

70% train, 15% validation, 15% test

More in Data Science

01Data AugmentationArtificially increasing training data by creating modified versions of existing data.02Data PreprocessingCleaning and transforming raw data into a format suitable for machine learning.

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