1. (17 pts) Suppose we train a model to predict whether an email isSpam or Not Spam. After training the model, we apply it to a test setof 500 new emails (also labeled) and the model produces thefollowing contingency table.True ClassNot Spam30SpamPredictedSpamNot Spam70Class70330i. Compute the precision of this model with respect to the Spamclassii. Compute the recall of this model with respect to the Spam classiii. Suppose we have two users with the following preferences.User 1 hates seeing spam emails in her inbox! However, shedoesn't mind periodically checking the "Junk" directory forgenuine emails incorrectly marked as spam.User 2 doesn't even know where the "Junk" directory is. Hewould much prefer to see spam emails in his inbox than to missgenuine emails without knowing!Which user is more likely to be satisfied with this classifier? Why?

formula of precision is equal to: True Positive / ( True Positive + False Positive) formula of…

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 Spam 70 70 Not Spam Predicted Class Spam Not Spam | i. Compute the precision of this model with respect to the Spam 30 330 class ii. Compute the recall of this model with respect to the Spam class

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 Spam 70 70 Not Spam Predicted Class Spam Not Spam | i. Compute the precision of this model with respect to the Spam 30 330 class ii. Compute the recall of this model with respect to the Spam class

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Suppose we train a model to predict whether an email is Spam or Not Spam. John always forgets to check his spam folder but doesn’t like missing any important messages at the same time. Will he prefer a high-precision and low recall classifier or a low precision and high-recall classifier? Explain
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