I have a large dataset describing numerous customers' behaviour and I am trying to solve a binary classification problem with a null accuracy on 90% (90/10 distribution amongst the two classes).
Given that I have computational limitations and thus are forced to take a subset of the sample, would it make sense for me to manipulate the balance to, let's say; 60/40 or 50/50 in my sample, now that I am limited to a fixed amount of total observations due to my hardware, just to "expose the machine learning algorithm to more of both classes" (from an marginal utility point of view)?
I have found multiple discussions about this online but not about this exact situation. I am very much aware of the fact that it would be optimal to just use ALL observations, and that it will mess up the true disitribution, but my rationale is that the problem is nothing like a poll sample but rather the idea of feeding the algorithm with more examples of observations that it haven't seen that many times.
Following guide states: "Consider testing under-sampling when you have a lot data (tens- or hundreds of thousands of instances or more)"
Would this impact the performance of the machine learning algorithm negatively and thus my prediction model so that I will get worse classifications on a 90/10 test set? And would someone be able to explain me why?