A validation set is a set of data used to train artificial intelligence (AI) with the goal of finding and optimizing the best model to solve a given problem. Validation sets are also known as dev sets.
A supervised AI is trained on a corpus of training data. Training, tuning, model selection and testing are performed with three different datasets: the training set, the validation set and the testing set. Validation sets are used to select and tune the final AI model.
Training sets make up the majority of the total data, averaging 60 percent. In testing, the models are fit to parameters in a process that is known as adjusting weights.
The validation set makes up about 20 percent of the bulk of data used. The validation set contrasts with training and test sets in that it is an intermediate phase used for choosing the best model and optimizing it. Validation is sometimes considered a part of the training phase. It is in this phase that parameter tuning occurs for optimizing the selected model. Overfitting is checked and avoided in the validation set to eliminate errors that can be caused for future predictions and observations if an analysis corresponds too precisely to a specific dataset.
Testing sets make up 20 percent of the bulk of the data. These sets are ideal data and results with which to verify correct operation of an AI. The test set is ensured to be the input data grouped together with verified correct outputs, generally by human verification. This ideal set is used to test results and assess the performance of the final model.
It is generally considered unwise to attempt further adjustment past the testing phase. Attempting to add further optimization outside the validation phase will likely to increase overfitting.