The validation set in the context of artificial intelligence and machine learning, refers to an independent dataset used to evaluate the ability of a trained model to generalise to previously unseen data.
Unlike the test set, the new or validation set is not used to tune the hyperparameters of the model, but is used to evaluate its final performance after the optimal hyperparameters have been selected. Therefore, the new or validation set is used to avoid over-fitting the test data and to obtain a more realistic assessment of the model's ability to generalise.
The new or validation set is used to select between alternative models and to tune the final model parameters prior to production deployment. The choice of the new or validation set and its appropriate size are critical to the model evaluation, as it must represent the data that the model will encounter in production.
Importantly, the new or validation set must also be independent of the training set and test set to ensure that the model has not previously seen the validation data during its training or pre-evaluation.
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