datasplitting

Data splitting is the practice of dividing a dataset into separate subsets for training, validation, and testing in order to assess how well a model will generalize to unseen data. The primary purpose is to provide an unbiased estimate of model performance and to prevent overfitting by separating the data used to learn from the data used to evaluate.

Common split schemes include holdout, k-fold cross-validation, stratified sampling, and time-series splits. In a holdout split, a portion of the data is set aside for testing, and sometimes a further subset is reserved for validation. K-fold cross-validation partitions the data into k folds and trains the model k times, each time with a different fold as the test set; results are averaged to reduce variance. Stratified sampling maintains the distribution of the target variable across splits, which is important for imbalanced classes. For time-series data, splits must respect temporal order, using forward chaining or rolling-origin approaches to avoid look-ahead bias.

Data splitting can be combined with preprocessing steps, but care must be taken to prevent data leakage, where information from the test set or future data informs the training process. Best practice is to perform splitting before preprocessing, fit scalers or imputers only on the training data, and apply them to the holdout sets.

Evaluation metrics vary by task, including accuracy, F1, or ROC-AUC for classification, and RMSE or MAE for regression. Nested cross-validation may be used when hyperparameter tuning is involved. Overall, data splitting is a fundamental tool for estimating predictive performance and guiding model selection.