# [under/over]-sampling teaches model the wrong distribution?

TLDR: Will under/oversampling during the training phase teach the model the wrong distribution and adversely affect accuracy?

Let us assume you want to train a classifier to differentiate between class A and class B. Unfortunately, the population distribution of A and B is unbalanced at a ratio of [1:100]. As such, you utilize under-sampling or over-sampling such that the training and validation sets effectively achieve a [1:1] ratio between A and B. You do nothing to the test set. The distributions of the sets and and the training results are in the below table:

           Train  Val    Test
A Dist.    0.5    0.5    0.99
B Dist.    0.5    0.5    0.01
Accuracy   1.0    0.999  0.85


You have now trained a model which performs worse on the population than a "classify all as A" approach. Does over-sampling or under-sampling teach a model the wrong distribution - causing it to over-confidently predict minority classes? If not - what could be happening in this example?