I see a few discussions that suggest downsampling is never correct for logistic regression or suggesting that you have to do bias term corrections post-hoc:
- Downsampling vs upsampling on the significance of the predictors in logistic regression
- Does down-sampling change logistic regression coefficients?
- Does an unbalanced sample matter when doing logistic regression?
- Should sampling for logistic regression reflect the real ratio of 1's and 0's?
The reasoning makes sense to me. However, I am training my logistic regression via SGD because of the amount of data I have (sparse features and a lot of samples). I am not able to get any meaningful convergence without downsampling.
If I primarily care about ordering and not about calibrated expectation of response variable is it okay to downsample in order for my SGD to converge?
Alternatively, I can set a very low learning rate and try to use all of my data but would rather avoid that if feasible.