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.