Stratified selection based on the y response creates a bias in information (Berkson's bias)? I have a database which has individuals in different months and a target variable which indicates wheter an event happened or not, let's say:

Id: 1, Month: 1, Event: 0
Id: 1, Month: 2, Event: 0
Id: 1, Month: 3, Event: 1
Id: 2, Month: 1, Event: 0
Id: 2, Month: 2, Event: 0


As my sample is so big (think of the number of combinations), I would like to choose a sample n much lesser than N (one month per individual).

For the individuals who had the event, the selected month is always the one which had the event. Always. There is no randomness.

Instead, for individuals which did not have the event, any month could be randomly selected.

These facts...


Selecting one month per individual.
Difference between selection methods for positives and negatives (I think is called Berkson's bias).


...affect the performance of the classification model?
It would be better to frame your problem as survival analysis, rather than classification. The goal of survival analysis is predict the duration until an event happens.