I am starting in Machine Learning, and I have doubts about some concepts. I've read we need to split our dataset into training, validation and test sets. I'll ask four questions related to them.
1 - Training set: It is used in
.fit() for our model to learn parameteres such as weights in a neural network?
2 - Validation set: Can also be used in
.fit(). The validation set is used so we can validate our model at the end of each epoch (to tune some hyperparameteres, like the number of nodes in a hidden layer)?
3 - If 2 is correct (i.e, the validation set was already used in
.fit()), do we still need to use
.evalute()? And why?
4 - Test set: New inputs (
x) never seen by the model, so i can make predictions on them? Used through the