validatieset

A validatieset, in machine learning and data science, is a subset of labeled data used during model development to estimate the model’s performance and to guide decisions about model selection and hyperparameter tuning. It sits between the training data and the final evaluation data, helping to assess how well a model trained on the training set generalizes to unseen data.

In practice, the available data are often split into three parts: training, validation, and test. The model is trained on the training set, and its performance is evaluated on the validation set to compare different models or to adjust hyperparameters. Once a model and its settings are chosen, the final assessment of its generalization capability is typically performed on the test set, which should remain untouched during development to provide an unbiased estimate of real-world performance.

Key considerations include avoiding data leakage between the sets, ensuring that splits respect the underlying data distribution (for example, stratifying by class labels in imbalanced datasets), and choosing an appropriate size for the validation set. When data are scarce, cross-validation or nested cross-validation can be used instead of a single validation split to obtain more reliable performance estimates. In time-series problems, the validation split should respect temporal order to prevent future information from leaking into past predictions. The choice of metrics on the validation set depends on the task, such as accuracy or AUC for classification, or RMSE and MAE for regression.