(b) Suppose that a machine prints a status message every minute. Each status message consists of one of the letters A,B,C,D,E or F depending on the machine's internal diagnostics. You suspect that there is a pattern that could predict the occurrence of the next status message being D based on the sequence the previous four (4) messages. You have available a large sequential stream of historical status messages produced by the machine over several days. How would you arrange or transform this data stream, creating appropriate attribute names where required, such that a decision tree learning task could be used to investigate this hypothesis. Further demonstrate this with the first 35 minutes of this status stream included below. B,D,E,A,D,E,E,B,C,B,A,E,E,B,D,B,D,B,F,A,A,B,B,D,D,B,E,E,A,D,E,C,E,E,F,...

(b) Suppose that a machine prints a status message every minute. Each status message consists of one of the letters A,B,C,D,E or F depending on the machine's internal diagnostics. You suspect that there is a pattern that could predict the occurrence of the next status message being D based on the sequence the previous four (4) messages. You have available a large sequential stream of historical status messages produced by the machine over several days. How would you arrange or transform this data stream, creating appropriate attribute names where required, such that a decision tree learning task could be used to investigate this hypothesis. Further demonstrate this with the first 35 minutes of this status stream included below. B,D,E,A,D,E,E,B,C,B,A,E,E,B,D,B,D,B,F,A,A,B,B,D,D,B,E,E,A,D,E,C,E,E,F,...

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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