How to solve this Consider the mutual information based feature selection Suppose we have the follow ing table (the entries in the table indicate counts) for the spam versus and non spam emails prize 1 prize 0 spam 130 15 spam 1200 13000 hello 1 hello 0 spam 160 25 spam 13000 7500 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 spam If any tools are used for your calculation, you must still show your mathematical steps in your report and include code files used for your calculations

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How to solve this Consider the mutual information based feature selection  Suppose we have the follow  ing table (the entries in the table indicate counts) for the spam versus and non spam emails    prize     1     prize     0   spam     130 15   spam     1200 13000   hello     1     hello     0   spam   160 25   spam   13000 7500 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 spam  If any tools are used for your calculation, you must still show your mathematical steps in your report and include code files used for your calculations

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Question: How to solve this Consider the mutual information-based feature selection. Suppose we have the follow- ing table (the entries in the table indicate counts) for

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