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Database System Concepts
7th Edition
ISBN: 9780078022159
Author: Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher: McGraw-Hill Education
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Binary classifications, classification error, partitioning attribute values

Transcribed Image Text:4. You are given the count matrix in the table below for the attribute "Previous GPA". The class label is
whether a student will pass(C1) or fail(CO) a class. We want to use the attribute "Previous GPA" as our
splitting attribute in an inner node of a decision tree with a binary test condition. Which is the best way
to partition the attribute values into two groups? Form the count matrices for the different partitions
and compute the classification error. Explain your reasoning while considering the metric of
classification error to evaluate the different partitions.
Previous GPA
Bad
Fair
Good
Very good
pass(C1)
fail(CO)
6
3
4
4
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