I read on Google's ML website if I have classification dataset with a ratio of 90% for one classification and 10% of the data for another classification.
In that case, should I use the exact same percentage of data for each classification? i.e. deleting around 80% of the dataset to make it 10% for each classification.
The reason is that Google said that the ML model will learn and then it is more likely to have a classification of the 90% and that won't provide good predictions. (i.e) The predictions might be biased towards a single label/feature.
My dataset is 90% to 10% but that is indeed the actual ratio and it is more likely to have the classification of the 90%
Shall I delete 80% of my data or keep it as is and let the ML learn that it is indeed more likely to have a classification of the 90%?