Is providing class weight to neural network enough for imbalanced binary classification? I have a highly imbalanced binary classification problem, probably 95:5 for two classes. I don't want to perform resampling as the data is already huge and training it would just take more time. (I'm also aware of down sampling)
But my question is , is providing class weights (let's say computed by scikit-learn's compute class weight) enough?
or there is any other method ?
model.fit(X,y,class_weight=class_weight)
Referring to an answer to a similar question, you don't have any reason to handle unbalance from the beginning. An imbalance of 95:5 isn't that big, I'd start with the regular training and if that doesn't work try more sophisticated things.