Data Splitting
Data splitting is a fundamental machine learning and statistical technique that involves partitioning a dataset into distinct subsets for training, validation, and testing models. It ensures that models are evaluated on unseen data to assess their generalization performance and prevent overfitting. Common splits include train-test splits, train-validation-test splits, and cross-validation folds.
Developers should use data splitting when building predictive models to validate performance reliably and avoid overfitting to training data. It is essential in supervised learning tasks like classification and regression, where unbiased evaluation is critical for model selection and hyperparameter tuning. Proper data splitting helps ensure that deployed models perform well on new, real-world data.