# MACHINE LEARNING

## APPLICATION OF SUPERVISED LEARNING

### CLASSIFICATION IN MACHINE LEARNING

 Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
Suppose I have 10, 000 emails in my mailbox out of which 300 are spams. The spam detection system detects 150 mails as spams, out of which 50 are actually spams. What is the precision and recall of my spam detection system?
 A Precision = 33.33%, Recall = 16.66% B Precision = 25%, Recall = 33.33% C Precision = 33.33%, Recall D Precision = 75%, Recall = 33.33%
Explanation:

Detailed explanation-1: -The spam detection system detects 150 mails as spams, out of which 50 are actually spams. What is the precision and recall of my spam detection system ? A. Precision = 33.33%, Recall = 16.66% B.

Detailed explanation-2: -1 It is estimated that 30% of emails are spam emails. Some software has been applied to filter these spam emails before they reach our inbox. A certain brand of software claims that it can detect 99% of spam emails, and the probability for a false positive (a non-spam email detected as spam) is 5%.

Detailed explanation-3: -One of the first factors email service providers will look at when determining if an email is spam is the IP address of the sender. If a specific IP address has received many complaints in the past, email from that address is more likely to be identified as spam.

Detailed explanation-4: -Machine learning algorithms use statistical models to classify data. In the case of spam detection, a trained machine learning model must be able to determine whether the sequence of words found in an email are closer to those found in spam emails or safe ones.

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