I'm reading the Book Jason Bell - Machine Learning - Hands-On for Developers and Technical Professionals. EAN 9781119642251 Verlag John Wiley & Sons
Mr. Bell has Examples in the Book which the reader is supposed to do on himself.
I have Problems with the Example of Chapter 5 - Using Weka to Create a Decision Tree. The target is to create a decision tree with WEKA Toolkit . I use Version 3.8.5. The training set is provided here .
The only steps I must do are the following:
And here my problem begins. My Output is completely different than the one in the book.
Book:
J48 pruned tree
------------------
placement = end_rack: yes (5.0/1.0)
placement = cd_spec
| pricing <= 80: yes (2.0)
| pricing > 80: no (2.0)
placement = std_rack
| eye_level = TRUE: yes (2.0)
| eye_level = FALSE: no (3.0)
Number of Leaves : 5
Size of the tree : 8
My Output:
J48 pruned tree
------------------
eye_level = TRUE: yes (6.0/2.0)
eye_level = FALSE: no (8.0/3.0)
Number of Leaves: 2
Size of the tree : 3
And that's completely different. :-DI don't understand why.
Has anybody done that example too? Have I missed an instruction step in the book? Or is a necessary setting not written in the book?
The supplied data file is wrong, if you change the data according to this you will get the same result as the book.
@relation ladygaga
@attribute placement {end_rack, cd_spec, std_rack}
@attribute prominence numeric
@attribute pricing numeric
@attribute eye_level {TRUE, FALSE}
@attribute customer_purchase {yes, no}
@data
end_rack,85,85,FALSE,yes
end_rack,80,90,TRUE,yes
cd_spec,83,86,FALSE,no
std_rack,70,96,FALSE,no
std_rack,68,80,FALSE,no
std_rack,65,70,TRUE,yes
cd_spec,64,65,TRUE,yes
end_rack,72,95,FALSE,yes
end_rack,69,70,FALSE,yes
std_rack,75,80,FALSE,no
end_rack,75,70,TRUE,no
cd_spec,72,90,TRUE,no
cd_spec,81,75,FALSE,yes
std_rack,71,91,TRUE,yes
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