Fine Line between Training set, Dev set, and Test set

[tbhaxor]

I also had this discussion when learning this course. Basically the cross validation data (not testing) is used to indirectly influence the learning of weights in the model.

Why do we need to have a validation set for training?

I was reading a blog after watching videos of the course in this week and found that cross validation set is used with model.fit and the information from this is often incorporated in the model during the training process. Arent we suppose to only use the training data X_train to optimise the weights? Also if Val set is used we can concatenate it with the X_train, something like model.fit(X_train.concat(X_val), y_train.concat(y_val)) Basically this line from the above mentioned article is no…

How I think is (using student and exam analogy),

• Training Data: Example from book / question bank
• Cross validation: Student checking his/her knowledge based on what they just learnt from the example, if they choose same examples they might be biased to the factors (keyword in question, ordering of question and etc), but actually not learning from these examples. They are just for personal check if they gained any information to solve the questions or not
• Testing Data: Mock test paper conducted by the coaching institutes