it’s a binary classification problem and the validation set is perfectly balanced (250 of class 0, 250 of class 1)
the training set is a bit screwed. about 35% are of class 1 there. I try to combat that using the WeightedRandomSampler to achieve implicit oversampling. beforehands I tried undersampling by just discarding the overage randomly. both results in more or less (± 1%) same results