Nominal variable= dummy-coded variable?

Question

Was wondering about this the other day. If I select a variable to be nominal, and it has X categories, is this mathematically equal to having manually edited the variable into n-1 (or n) dummy categories?

That is, if I use a method that accepts inputs of nominal measures, is there any difference if the nominal variable has been split into a group of binomial variables or not?

-Frankie

land · Answer

Hi,
RapidMiner does not transform any nominal typed variables to numerical ones if the learner does need this! We are sticking to the believe that the user should be in full control over what the process does.
In fact it would be easy adding this, but since the dummy encoding of nominal variables can have a major impact on the (prediction) performance, we won't do this implicitly. Instead we are presenting the user QuickFixes with which the user is capable of solving the problem with one click.

Greetings,
  Sebastian

wessel · Answer

frankie  wrote:
Any learner actually, my question is more concerned with how RM deals with a variable that has been marked as nominal.

RM tried to stay as closely as possible to the algorithm presented in the paper that introduced the learning algorithm.
So if you use for example the algorithm ID3, it does not make dummies at all, it calculates information gain directly on the original attributes.

frankie · Answer

Any learner actually, my question is more concerned with how RM deals with a variable that has been marked as nominal.