A Validation Set is a held-out partition of data, distinct from the training and test sets, used to tune hyperparameters and make model-selection decisions during machine learning development. By evaluating candidate models on data not used for fitting, it provides an unbiased signal for choices such as architecture, regularisation strength and early stopping. The test set is reserved for a final, untouched estimate of generalisation.
Overview
- A Validation Set is a held-out partition of data, distinct from the training and test sets, used to tune hyperparameters and make model-selection decisions during machine learning development.
- By evaluating candidate models on data not used for fitting, it provides an unbiased signal for choices such as architecture, regularisation strength and early stopping.
- The test set is reserved for a final, untouched estimate of generalisation.
- It is modelled as a subclass of Model Evaluation within the machine-learning domain.
Key aspects
- Accuracy is a constituent or mechanism relevant to Validation Set.
- Loss Function is a constituent or mechanism relevant to Validation Set.
Mechanisms
- Validation Set enables Hyperparameter Tuning.
- Validation Set enables Model Selection.
- Validation Set enables Early Stopping.
- Validation Set supports Model Training.
- Validation Set supports Bias Variance Tradeoff.
Applications
- Applied in contexts involving Hyperparameter Tuning.
- Applied in contexts involving Model Selection.
- Applied in contexts involving Early Stopping.
- Applied in contexts involving Model Training.
- Applied in contexts involving Bias Variance Tradeoff.
- Applied in contexts involving Overfitting.