What is validation set used for

We test the accuracy of our selected model on test set.Again some practical issues here:In other words, validation process means, finding the best possible model parameters, that could yield best results.It is okay if your all collected data are not from the same domain where the model will be used.So just before training, we keep aside a small portion of the training set.

Used to test the model's final ability to generalize before deploying to production.The validation set is used just to give an indication about the network's performance.Hence the model occasionally sees this data, but never does it learn from this.Used during training to check how well the model is generalizing.What is the use of validation set?

This process is called validation.The testing set evaluates how well the tuned model can make predictions on unseen data.In other words, validation process means, finding the best possible model parameters, that could yield best results.The part of the dataset to evaluate the final overall model performance.The validation set is used for hyperparameter tuning.

In reality you need a whole hierarchy of test sets.The following diagram provides a visual explanation of these three different types of datasets:Used to optimize model parameters.Used to get an unbiased estimate of the final model performance.The validation set evaluates the model's performance on unseen data and is useful when tuning the model's hyperparameters.