Consider the mutual information based feature selection Suppose we have the follow ing table (the entries in table indicate counts) for the spam versus and non spam emails prize 1 prize 0 spam 1 150 10 spam 0 1000 15000 hello 1 hello 0 spam 1 155 5 spam 0 14000 1000 Given the two tables above, calculate the mutual information for the two keywords, prize and hello respectively Which keyword is more informative for deciding whether or not the email is a spam

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Question: Consider the mutual information based feature selection. Suppose we have the follow- ing table (the entries in table indicate counts) for the spam versus and

Consider the mutual information based feature selection. Suppose we have the follow- ing table (the entries in table indicate counts) for the spam versus and non-spam emails:

	"prize"=1	"prize"=0
"spam"=1	150	10
"spam"=0	1000	15000

	"hello"=1	"hello"=0
"spam"=1	155	5
"spam"=0	14000	1000

Given the two tables above, calculate the mutual information for the two keywords, "prize" and "hello" respectively. Which keyword is more informative for deciding whether or not the email is a spam?

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