meachine learning 第六周 2

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 good classifier should have both a

high precision and high recall on the cross validation

set.

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.

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.

If you always predict non-spam (output

y=0), your classifier will have an accuracy of

99%.

选 A C D  是错误的，正解未知