1. (17 pts) Suppose we train a model to predict whether an email is Spam or Not Spam. After training the model, we apply it to a test set of 500 new emails (also labeled) and the model produces the...

Extracted text: 1. (17 pts) Suppose we train a model to predict whether an email is Spam or Not Spam. After training the model, we apply it to a test set of 500 new emails (also labeled) and the model produces the following contingency table. True Class Not Spam 30 Spam Predicted Spam Not Spam 70 Class 70 330 i. Compute the precision of this model with respect to the Spam class ii. Compute the recall of this model with respect to the Spam class iii. Suppose we have two users with the following preferences. User 1 hates seeing spam emails in her inbox! However, she doesn't mind periodically checking the "Junk" directory for genuine emails incorrectly marked as spam. User 2 doesn't even know where the "Junk" directory is. He would much prefer to see spam emails in his inbox than to miss genuine emails without knowing! Which user is more likely to be satisfied with this classifier? Why?