Suppose you are working on a spam classifier, where spam emails are positive examples (y=1) and non-spam emails are negative examples (y=0). you have a training set of emails in which 99% of the emails are non-spam and the other 1% is spam. which of the following statements are true?
check all that apply.
a. if you always predict non-spam (output y=0), your classifier will have 99% accuracy on the training set, and it will likely perform similarly on the cross validation set. b.if you always predict non-spam (output y=0), your classifier will have an accuracy of 99%.c. a good classifier should have both a high precision and high recall on the cross validation set. d.if you always predict non-spam (output y=0), your classifier will have 99% accuracy on the training set, but it will do much worse on the cross validation set because it has overfit the training data.