A validation set can be considered part of the training set as it is used for parameter selection and to avoid overfitting the model being built. On the other hand, a test set is used for testing or evaluating the performance of a trained machine learning model.
In simple terms, the differences can be summarised as :
* Training Set is to fit the parameters, i.e. weights.
* Test Set is to assess the performance of the model, i.e. evaluating the predictive power and generalisation.
* The validation set is to tune the parameters.
