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There are 14 instances stored in the database described with several attributes: day, outlook, temperature, humidity, wind and 'playTennis'. Each instance describes the facts of the day and the action of the observed person (played or not played tennis). Based on the given record we can assess which factors affected the person's decision about playing tennis. Training Examples Outlook Day Sunny Temp. Humidity High High Wind Play Tennis No D1 Hot Weak D2 D3 Strong Weak Weak Weak Strong Weak Weak No Sunny Overcast Rain Hot Hot Yes D4 D5 High High Normal Normal Mild Yes Yes No Rain |Cool D6 Rain Сol D7 Overcast Cool Normal Yes D8 Sunny Sunny Mild No High Normal Normal Normal High Normal D9 Cool Yes Weak Strong Strong Strong Weak D10 Rain Mild Yes D11 Sunny D12 Overcast D13 Overcast Rain Mild Mild Yes Yes Yes Hot D14 Mild High Strong No

There are 14 instances stored in the database described with several attributes: day, outlook, temperature, humidity, wind and 'playTennis'. Each instance describes the facts of the day and the action of the observed person (played or not played tennis). Based on the given record we can assess which factors affected the person's decision about playing tennis. Training Examples Outlook Day Sunny Temp. Humidity High High Wind Play Tennis No D1 Hot Weak D2 D3 Strong Weak Weak Weak Strong Weak Weak No Sunny Overcast Rain Hot Hot Yes D4 D5 High High Normal Normal Mild Yes Yes No Rain |Cool D6 Rain Сol D7 Overcast Cool Normal Yes D8 Sunny Sunny Mild No High Normal Normal Normal High Normal D9 Cool Yes Weak Strong Strong Strong Weak D10 Rain Mild Yes D11 Sunny D12 Overcast D13 Overcast Rain Mild Mild Yes Yes Yes Hot D14 Mild High Strong No

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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There are 14 instances stored in the database described with several attributes: day, outlook, temperature, humidity, wind and 'playTennis'. Each instance describes the facts of the day and the action of the observed person (played or not played tennis). Based on the given record we can assess which factors affected the person's decision about playing tennis. Training Examples Play Tennis No Outlook Day D1 Sunny D2 Sunny Temp. Humidity High High High High Normal Wind Hot Weak Hot Strong No D3 Overcast Hot Weak Yes D4 D5 Rain Rain Mild Сool Weak Yes Weak Yes D6 Rain Cool Normal Strong Weak No D7 Overcast Cool Normal Yes D8 High Normal Sunny Mild Weak No D9 Sunny D10 Rain D11 Sunny D12 Overcast D13 Overcast D14 Cool Weak Yes Normal Normal Mild Strong Yes Mild Strong Strong Weak Yes Mild Hot Mild High Normal Yes Yes No Rain High Strong Calculate the information gain values of all attributes, and select the decision attribute for the root node, and create branches with its possible values. Is there any further splitting necessary? If so, keep splitting and build the final tree (do not worry about over-fitting). Provide all your calculations.
Transcribed Image Text:There are 14 instances stored in the database described with several attributes: day, outlook, temperature, humidity, wind and 'playTennis'. Each instance describes the facts of the day and the action of the observed person (played or not played tennis). Based on the given record we can assess which factors affected the person's decision about playing tennis. Training Examples Play Tennis No Outlook Day D1 Sunny D2 Sunny Temp. Humidity High High High High Normal Wind Hot Weak Hot Strong No D3 Overcast Hot Weak Yes D4 D5 Rain Rain Mild Сool Weak Yes Weak Yes D6 Rain Cool Normal Strong Weak No D7 Overcast Cool Normal Yes D8 High Normal Sunny Mild Weak No D9 Sunny D10 Rain D11 Sunny D12 Overcast D13 Overcast D14 Cool Weak Yes Normal Normal Mild Strong Yes Mild Strong Strong Weak Yes Mild Hot Mild High Normal Yes Yes No Rain High Strong Calculate the information gain values of all attributes, and select the decision attribute for the root node, and create branches with its possible values. Is there any further splitting necessary? If so, keep splitting and build the final tree (do not worry about over-fitting). Provide all your calculations.
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