traintestsplit

traintestsplit, commonly written as train_test_split, is a utility function in scikit-learn's model_selection module used to partition data into training and testing subsets. It accepts arrays or matrices (for example feature data X and target y) and returns corresponding splits that can be used to fit and evaluate a model. By default, the function shuffles the data before splitting, and it can produce a train and a test set in a single call. If stratify is provided, the split preserves the distribution of the target variable across both subsets; setting random_state ensures reproducibility of the split.

Key parameters include test_size, which specifies the proportion or absolute number of samples in the test set; train_size, for the training set; random_state, which controls randomness; shuffle, which toggles shuffling before splitting (default True); and stratify, which preserves label distributions.

The function returns the splits in the same order as the inputs, so passing X and y yields X_train, X_test, y_train, y_test. Additional inputs produce corresponding splits.

Usage considerations: for imbalanced classification problems, using stratify helps maintain class proportions; for reproducibility, fix random_state. For time-series data, disable shuffling or use time-series specific splitting strategies rather than a random split. Train-test split is a simple holdout method and is often used as a baseline before more elaborate cross-validation schemes such as KFold or StratifiedKFold.