I am learning the SVM classification and encounter a problem. I am not sure if this dilemma has a terminology for it.
Assume we would like to classify patient by SVM given the samples of healthy people ( of both gender) and people with liver cancer ( of both gender). If we label healthy people sample as class 1 and the people with cancer as class 2, we can train a binary SVM and obtain a classifier 1 to predict any new patient. Now, image another scenario. Assume that we first divide all samples by gender before SVM classification. For each gender, we still label healthy patients vs cancerous patients into 2 classes and train a binary SVM to obtain classifier 2 and classifier 3 for female and male samples respectively. The question is if there is a new female patient, which classifier, 1 or 2, should be used to obtain more accurate prediction ? Here is the dilemma for the arguments I have
(1) When the number of samples is large, the prediction should be more accurate. Based on this argument, the classifier 1 seems a good choice.
(2) However, if we divide samples into female and male groups first, the classifier 2 seems a better choice since the new patient (unknown test sample) is female.
Does this kind of dilemma have a terminology or does anyone know any further information or how to solve problem like this ? I am not even sure if this is a legit question and sorry for the naive question in advance. Thanks