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.
Cloud computing services or solutions, whether in Spain or anywhere else in the world, are infrastructures, platforms or systems that are used in the cloud.
Read More »Artificial Intelligence (AI) derives from a series of models or branches that can be used in different areas of people's lives, as well as in different areas of [...]
Read More »There is a consensus among executives of the world's largest companies about the important impact that Artificial Intelligence (AI) will have on the [...]
Read More »The massive implementation of cloud services in companies has transformed the way in which business transactions were carried out, since it has [...]
Read More »
