Use the data provided in the table Table 1. Add one more attribute, that is the averagesubscriber total talk time. Discretize each attribute by splitting it into three equal parts e.g.high; low; mid. The applicable attributes now are Calls, SMS, Average Total talk time. Usingthe ID3 decision tree algorithm, establish the root node of your decision tree. ) The data in the table 1 was collected from random mobile phone users (subscribers) alongUniversity Way, Nairobi. The attributes are, Calls: average daily number of calls made;Length: average length of phone calls made in minutes; SMS messages: average dailynumber of short message service (SMS) sent. No details of received calls and received SMSmessages was collected.SubscriberCallsLengthSMSCustomer Class11.90.75.1student6.51.57.2employee31.50.95.6student49.20.94.5employee55.12.210.2employee1.70.64.5student73.50.45.3student812.10.52.5employee98.20.84.9employee106.10.44.9studentTable 1: subscriber data

Classification is a two-step process, learning step and prediction step, in machine learning. In the…

Answered: Use the data provided in the table…

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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Use the data provided in the table Table 1. Add one more attribute, that is the average
subscriber total talk time. Discretize each attribute by splitting it into three equal parts e.g.
high; low; mid. The applicable attributes now are Calls, SMS, Average Total talk time. Using
the ID3 decision tree algorithm, establish the root node of your decision tree.