Thank you for all the answers. Yes, I'm not dealing with arbitrary strings,
just a set of possible values, so the binary representation seems OK.
One more way would be computing classification probability for each value
and plugging the resulting number back into data. For example, let's say
there are 10 samples with BMW in the training set, and 3 of them are 1
(true), 7 are 0 (false). So the maximum likelihood of BMW sample being true
is 0.3, and we'd put 0.3 instead of BMW in these 10 samples.
What do you think, is it sound matematically?
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