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Google Cloud Certified Professional Machine Learning Engineer · Google Cloud
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C - This option combines two techniques: downsampling and upweighting. While a 1:1 ratio is often the goal, a 10% positive example ratio is a significant improvement from the original less than 1% and can be a practical compromise.
answer is C. it can converge without discarding all useful negative samples. A is not clear about how it's generating the samples, might cause overfitting. B is not directly related to sample issue. D too extreme of undersampling
aha the problem says less than 1% are negative examples so making them 1:1 would eleminate the example cases too much i think i understood