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P(PlayTennis-no) = P(Outlook=rain|PlayTennis=yes) = P(Outlook=rain|PlayTennis=no) = 5/14 = 0.36 [Choose ] [Choose ] P(Temperature mild | PlayTennis=yes) = [Choose ] P(Temperature=mild | PlayTennis-no) = [Choose ] P(Humidity=high| PlayTennis=yes) = [Choose ] P(Humidity=high| PlayTennis=no) = [Choose ] P(Wind-strong|PlayTennis=yes) = P(Wind-strong|PlayTennis=no) = [Choose ] [Choose ] P(PlayTennis=yes, Outlook-rain, Temperature-mild, Humidity=high, Wind strong) = [Choose ] P(PlayTennis=no, Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = [Choose ] P(PlayTennis=yes|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = [Choose ] P(PlayTennis-no|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = { [Choose ] Will the player play tennis on the given day? [Choose ] Using the naive Bayes classifier approach, decide if a person will/will not play tennis on a day that is: Outlook=rain Temperature-mild Humidity=high Wind-strong Use the "PlayTennis" dataset that was used in class (probabilities.pptx). Calculate the prior probabilities P(PlayTennis=yes) = 9/14 = 0.64 P(PlayTennis-no) = Calculate the conditional probabilities P(Outlook-rain|PlayTennis=yes) = P(Outlook-rain|PlayTennis=no) = P(Temperature=mild|PlayTennis=yes) = P(Temperature=mild|PlayTennis=no) = P(Humidity=high|PlayTennis=yes) = P(Humidity=high|PlayTennis=no) = P(Wind-strong|PlayTennis=yes) = P(Wind-strong|PlayTennis=no) = Calculate the joint probabilities (before normalization) P(PlayTennis=yes, Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = P(PlayTennis=no, Outlook-rain, Temperature=mild, Humidity=high, Wind=strong) = Calculate the conditional probabilities (after normalization) P(PlayTennis=yes|Outlook=rain, Temperature=mild, Humidity=high, Wind=strong) = P(PlayTennis-no|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = Based on the above joint probabilities, is the player likely to play tennis on the given day?

P(PlayTennis-no) = P(Outlook=rain|PlayTennis=yes) = P(Outlook=rain|PlayTennis=no) = 5/14 = 0.36 [Choose ] [Choose ] P(Temperature mild | PlayTennis=yes) = [Choose ] P(Temperature=mild | PlayTennis-no) = [Choose ] P(Humidity=high| PlayTennis=yes) = [Choose ] P(Humidity=high| PlayTennis=no) = [Choose ] P(Wind-strong|PlayTennis=yes) = P(Wind-strong|PlayTennis=no) = [Choose ] [Choose ] P(PlayTennis=yes, Outlook-rain, Temperature-mild, Humidity=high, Wind strong) = [Choose ] P(PlayTennis=no, Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = [Choose ] P(PlayTennis=yes|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = [Choose ] P(PlayTennis-no|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = { [Choose ] Will the player play tennis on the given day? [Choose ] Using the naive Bayes classifier approach, decide if a person will/will not play tennis on a day that is: Outlook=rain Temperature-mild Humidity=high Wind-strong Use the "PlayTennis" dataset that was used in class (probabilities.pptx). Calculate the prior probabilities P(PlayTennis=yes) = 9/14 = 0.64 P(PlayTennis-no) = Calculate the conditional probabilities P(Outlook-rain|PlayTennis=yes) = P(Outlook-rain|PlayTennis=no) = P(Temperature=mild|PlayTennis=yes) = P(Temperature=mild|PlayTennis=no) = P(Humidity=high|PlayTennis=yes) = P(Humidity=high|PlayTennis=no) = P(Wind-strong|PlayTennis=yes) = P(Wind-strong|PlayTennis=no) = Calculate the joint probabilities (before normalization) P(PlayTennis=yes, Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = P(PlayTennis=no, Outlook-rain, Temperature=mild, Humidity=high, Wind=strong) = Calculate the conditional probabilities (after normalization) P(PlayTennis=yes|Outlook=rain, Temperature=mild, Humidity=high, Wind=strong) = P(PlayTennis-no|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = Based on the above joint probabilities, is the player likely to play tennis on the given day?

Database System Concepts
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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P(PlayTennis-no) = P(Outlook=rain|PlayTennis=yes) = P(Outlook=rain|PlayTennis=no) = 5/14 = 0.36 [Choose ] [Choose ] P(Temperature mild | PlayTennis=yes) = [Choose ] P(Temperature=mild | PlayTennis-no) = [Choose ] P(Humidity=high| PlayTennis=yes) = [Choose ] P(Humidity=high| PlayTennis=no) = [Choose ] P(Wind-strong|PlayTennis=yes) = P(Wind-strong|PlayTennis=no) = [Choose ] [Choose ] P(PlayTennis=yes, Outlook-rain, Temperature-mild, Humidity=high, Wind strong) = [Choose ] P(PlayTennis=no, Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = [Choose ] P(PlayTennis=yes|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = [Choose ] P(PlayTennis-no|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = { [Choose ] Will the player play tennis on the given day? [Choose ]
Transcribed Image Text:P(PlayTennis-no) = P(Outlook=rain|PlayTennis=yes) = P(Outlook=rain|PlayTennis=no) = 5/14 = 0.36 [Choose ] [Choose ] P(Temperature mild | PlayTennis=yes) = [Choose ] P(Temperature=mild | PlayTennis-no) = [Choose ] P(Humidity=high| PlayTennis=yes) = [Choose ] P(Humidity=high| PlayTennis=no) = [Choose ] P(Wind-strong|PlayTennis=yes) = P(Wind-strong|PlayTennis=no) = [Choose ] [Choose ] P(PlayTennis=yes, Outlook-rain, Temperature-mild, Humidity=high, Wind strong) = [Choose ] P(PlayTennis=no, Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = [Choose ] P(PlayTennis=yes|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = [Choose ] P(PlayTennis-no|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = { [Choose ] Will the player play tennis on the given day? [Choose ]
Using the naive Bayes classifier approach, decide if a person will/will not play tennis on a day that is: Outlook=rain Temperature-mild Humidity=high Wind-strong Use the "PlayTennis" dataset that was used in class (probabilities.pptx). Calculate the prior probabilities P(PlayTennis=yes) = 9/14 = 0.64 P(PlayTennis-no) = Calculate the conditional probabilities P(Outlook-rain|PlayTennis=yes) = P(Outlook-rain|PlayTennis=no) = P(Temperature=mild|PlayTennis=yes) = P(Temperature=mild|PlayTennis=no) = P(Humidity=high|PlayTennis=yes) = P(Humidity=high|PlayTennis=no) = P(Wind-strong|PlayTennis=yes) = P(Wind-strong|PlayTennis=no) = Calculate the joint probabilities (before normalization) P(PlayTennis=yes, Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = P(PlayTennis=no, Outlook-rain, Temperature=mild, Humidity=high, Wind=strong) = Calculate the conditional probabilities (after normalization) P(PlayTennis=yes|Outlook=rain, Temperature=mild, Humidity=high, Wind=strong) = P(PlayTennis-no|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = Based on the above joint probabilities, is the player likely to play tennis on the given day?
Transcribed Image Text:Using the naive Bayes classifier approach, decide if a person will/will not play tennis on a day that is: Outlook=rain Temperature-mild Humidity=high Wind-strong Use the "PlayTennis" dataset that was used in class (probabilities.pptx). Calculate the prior probabilities P(PlayTennis=yes) = 9/14 = 0.64 P(PlayTennis-no) = Calculate the conditional probabilities P(Outlook-rain|PlayTennis=yes) = P(Outlook-rain|PlayTennis=no) = P(Temperature=mild|PlayTennis=yes) = P(Temperature=mild|PlayTennis=no) = P(Humidity=high|PlayTennis=yes) = P(Humidity=high|PlayTennis=no) = P(Wind-strong|PlayTennis=yes) = P(Wind-strong|PlayTennis=no) = Calculate the joint probabilities (before normalization) P(PlayTennis=yes, Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = P(PlayTennis=no, Outlook-rain, Temperature=mild, Humidity=high, Wind=strong) = Calculate the conditional probabilities (after normalization) P(PlayTennis=yes|Outlook=rain, Temperature=mild, Humidity=high, Wind=strong) = P(PlayTennis-no|Outlook=rain, Temperature=mild, Humidity=high, Wind-strong) = Based on the above joint probabilities, is the player likely to play tennis on the given day?
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