So far I think I understood the differences between the training, test and validation set. Basically it is like in this image:
Training set: The data where the model is trained on
Validation set: Data the model has not been trained on and used to tune hyperparameters
Test set: In principle the same like the validation set.. just used at the final end after the model has been tailored.
The training set is usually set up via some cross validation. When fitting via
model.fit(X_train, y_train,..) will Keras shuffle the data autonomously ?
Next, in Keras, you are able to provide the validation set inside the
model.fit() method as
validation_data=(x_test, y_test) but there is also the possibility to provide e.g.
validation_split = 0.2
What is the difference?
And after that, the test/prediction set will be taken into account just as
model.pred(X_pred, y_pred,..). Right?