I'm learning an new concept in machine learning can you help me to figure out which one is more optimal to choose and why??

+22

Which method would you prefer among L2 regularization, dropout technique, early stopping to deal with the over fitting issue of your deep learning model ?

1/24/2021 6:38:40 AM

Preity

3 Answers

New Answer

Sort by:

Well I don't know much though it's in my fourth year..but as far as I can understand.. There are two ways to approach an overfit model: >Reduce overfitting by training the network on more examples. >Reduce overfitting by changing the complexity of the network.

Aditya(Less active)

Without knowing your model, it's hard to give any proper advice. But early stopping seems to be kind of a universal weapon of choice in trying to handle overfitting issues. Combinations of methods are not uncommon though. This might be helpful: https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/

SpaceBarMönkey™

What's your model? - L2 is ridge regression to eliminate co-linearity - Dropout is drop neurones randomly to make the model simpler - Early stopping stops the iterations before overfitting because your algorithm runs well under certain number of iterations. What's your case?

David Delgado