When to use validation, validation and test sets?

For Dataset B, the validation set would be used to test the trained model, and the test set would evaluate the final model. The data used to build the final model usually comes from multiple datasets. In particular, three datasets are commonly used in different stages of the creation of the model.
And, what's the difference between validation and test sets?
The validation set is also known as the Dev set or the Development set. This makes sense since this dataset helps during the “development” stage of the model. Test Dataset: The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
Additionally, why is it important to validate validation set during training? During training, if we're also validating the model on the validation set and see that the results it's giving for the validation data are just as good as the results it's giving for the training data, then we can be more confident that our model is not overfitting.
In addition, which is an example of a validation dataset?
A validation dataset is a dataset of examples used to tune the hyperparameters (i.e. the architecture) of a classifier. It is sometimes also called the development set or the "dev set". An example of a hyperparameter for artificial neural networks includes the number of hidden units in each layer.
Similarly, what is the purpose of cross validation set?
The cross validation set is used to help detect over-fitting and to assist in hyper-parameter search. The test set is used to measure the performance of the model.

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