How do you handle data imbalance in SVM?
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If I am training a SVM on a lrge training set and if the class variable is either True or False, would having very few True values compared to he number of False values in the training set affect the training model/results? Should they be equal? If my training set doesn't have an equal distribution of True and False, how do I take care of this such that my training is done as efficiently as possible?