https://app.dataquest.io/m/432/the-naive-bayes-algorithm/3/using-bayes-theorem

I was wondering if anyone could help me put this into layman’s terms. I understand the trick on how to apply the calculations to reach the desired result but I do not exactly get what the 3 “new_message” parameters mean (see below).

I understand that the probability of a new message being spam or non spam both equal 0.5. What I do not understand is what the p_new_message means.

‘’’

p_spam = 0.5

p_non_spam = 0.5

p_new_message = 0.5417

p_new_message_given_spam = 0.75

p_new_message_given_non_spam = 0.3334

‘’’

My unconfirmed assumptions are:

p_spam = P of any new message being spam

p_spam = P of any new message not being spam

p_new_message = P of a new specific message being spam based on its contents

p_new_message_given_spam = P of this message having been correctly assigned to spam

p_new_message_given_non_spam = P of this message having been correctly assigned to non spam

Sorry I’ve been posting many questions lately…

thank you in advance!