How can I deal with categorial features inside linear ML algorithms? The obvious solution is to represent each value as a binary feature. For example, if the categorical feature color has three possible values: red, blue and green, then we replace it with three binary features [color == red], [color == green], [color == blue]. Are there better solutions?
Specifically, I am using Vowpal Wabbit's logistic regression. As far as I understood from the tutorial VW can operate with categorial features, but I did not find out how does in work.