testsize

Testsize is a term used in data science to describe the portion of a dataset reserved for evaluating a model, typically in the context of a train–test split. In many machine learning libraries, the related parameter is named test_size and can be given as a float between 0 and 1 (representing a fraction of the data) or as an explicit integer (representing a number of samples). The exact interpretation depends on the library but the core idea is the same: specify how much data will be held out for testing.

In a holdout validation workflow, the data are partitioned into a training set and a test set. The test size fraction indicates how much of the data is withheld for evaluation, while the remainder is used for model training. Common choices range from 0.2 to 0.3, with 0.25 being a frequent default in several libraries. If both train_size and test_size are provided, their values determine the split, subject to the total not exceeding the dataset size.

Practical considerations include ensuring reproducibility by fixing a random seed, using stratified sampling for imbalanced classes, and avoiding data leakage where information from the test set inadvertently influences the training process. For more robust performance estimates, practitioners may prefer cross-validation or repeated random sub-sampling validation over a single holdout split. Additionally, the choice of test_size interacts with model complexity and dataset size: a larger test set reduces the training data and can affect learning, while a smaller test set may give noisier evaluation.