Trainingvalidation

Trainingvalidation refers to the practice in machine learning of partitioning data into subsets for model development, typically including at least a training set and a validation set. The goal is to train a model on one portion of the data and assess its performance on another to gauge generalization and guide decisions such as hyperparameter tuning and feature engineering, while reducing the risk of overfitting to the training data.

Common approaches include the hold-out method, where data is split into training and validation sets (for example 80/20 or 70/30). K-fold cross-validation partitions the data into k folds, training on k-1 folds and validating on the remaining fold, with results averaged across folds. Stratified variants ensure class distribution is preserved in each fold. For sequential or time-dependent data, time-series cross-validation or forward-chaining methods are used to respect temporal order. Nested cross-validation combines an inner loop for hyperparameter tuning with an outer loop for unbiased performance estimation, reducing optimistic bias.

Best practices emphasize avoiding data leakage between training and validation stages, using random seeds and stratification where appropriate, and aligning the validation strategy with the problem type and data size. Early stopping in iterative models (such as neural networks) often uses validation performance to determine when to halt training.

Metrics selected for validation depend on the task: accuracy, precision, recall, F1, and ROC-AUC for classification; mean squared error or mean absolute error for regression; calibration measures for probability estimates. After finalizing model choices, a separate held-out test set is typically used for an final, unbiased evaluation of expected real-world performance.