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Normalization :

  • All numeric attribute values result in being in the interval [0, 1].
  • The data sets, both training and test, were normalized at the same time so that the results would be consistent between the data sets.

Discretization :

  • The same method used for Project 2: Decision Trees was used here. The only difference being that continuous attributes could be split into more than two bins.

Missing Values :

  • These were simply skipped over for the purpose of generating the probabilities for the classifier. It is the equivelant of making each missing value contribute a factor of 1 to the probablity, in effect, changing nothing.
  • This same approach was used in classifing the test exmaples.

 

by: Keith A. Pray
Last Modified: January 10, 2007 3:22 PM
© 2007 - 1975 Keith A. Pray.
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