Say I have regression problem which is to predict score between [0,1] and data is really skewed.
And train data set and test data set, # of regression answer label 0 is 10% and 1 is 90% (so I know prior distribution of 0s and 1s in both test and train dataset)
But what I really want to focus is model to predict 0 well. But in training data there is so many 1's so deep regression model tend to fit to train on 1, so even if answer is 0, it outputs 0.5.
Is it okay to train model to see 0 more (over-sampling) and will it solve the problem? (I'm expecting possible increased total loss but less error on label 0)
I'm using MLP model trained with SGD adam optimizer.
Since I know the prior distribution and real goal is to predict 0 with less error, I'm thinking of each batch to see 0 value examples with increased possibility. Could there be another smarter way?