Suppose a Bayesian spam filter is trained on a set of 100,000 spam emails and 4,000 emails that are not spam. The word "money" appears in 2,500 spam emails and 200 nonspam emails, the word "urgent" appears in 8,000 spam emails and 800 nonspam emails, the word "attention" appears in 20,000 spam emails and 80 nonspam emails, and the word "account" appears in 10,000 spam emails and 100 nonspam emails. Estimate the probability that a received email containing all four words of "money", "urgent", "attention", and "account" is spam. Will the email be rejected as spam if the threshold for rejecting spam is set at 80%?

Suppose a Bayesian spam filter is trained on a set of 100,000 spam emails and 4,000 emails that are not spam. The word "money" appears in 2,500 spam emails and 200 nonspam emails, the word "urgent" appears in 8,000 spam emails and 800 nonspam emails, the word "attention" appears in 20,000 spam emails and 80 nonspam emails, and the word "account" appears in 10,000 spam emails and 100 nonspam emails. Estimate the probability that a received email containing all four words of "money", "urgent", "attention", and "account" is spam. Will the email be rejected as spam if the threshold for rejecting spam is set at 80%?

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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