A classification tree is built on the data below to predict whether or not a student will pass a course. The predictor variables include the categorisation of 2nd year marks (categorised as high (H) / medium (M) / or low (L)) and whether or not the majority of lectures were attended (yes (Y) or no (N)). The response is coded as a pass (P) or fail (F). 2nd yr marks Attended Passed F Y P M F M Y P H H Y The Shannon-entropy value for passing given the lecture attendance variable is P.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.6: Summarizing Categorical Data
Problem 27PPS
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A classification tree is built on the data below
to predict whether or not a student will pass a
course. The predictor variables include the
categorisation of 2nd year marks (categorised
as high (H) / medium (M) / or low (L)) and
whether or not the majority of lectures were
attended (yes (Y) or no (N)). The response is
coded as a pass (P) or fail (F).
2nd yr marks Attended Passed
N
F
Y
P
M
F
M
Y
P
H
H
Y
P
The Shannon-entropy value for passing given
the lecture attendance variable is
Transcribed Image Text:A classification tree is built on the data below to predict whether or not a student will pass a course. The predictor variables include the categorisation of 2nd year marks (categorised as high (H) / medium (M) / or low (L)) and whether or not the majority of lectures were attended (yes (Y) or no (N)). The response is coded as a pass (P) or fail (F). 2nd yr marks Attended Passed N F Y P M F M Y P H H Y P The Shannon-entropy value for passing given the lecture attendance variable is
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