I'm struggling to find a method for reducing the number of categories in nominal or ordinal data.
For example, let's say that I want to build a regression model on a dataset that has a number of nominal and ordinal factors. While I have no problems with this step, I often run into situations where a nominal feature is without observations in the training set, but subsequently exists in the validation dataset. This naturally leads to and error when the model is presented with (so far) unseen cases. Another situation where I would like to combine categories is simply when there are too many categories with few observations.
So my questions are:
- While I realize it might be best to combine many nominal (and ordinal) categories based on the prior real-world background information they represent, are there systematic methods (
Rpackages preferably) available?
- What guidelines and suggestions would you make regarding, cut-off thresholds and so on?
- What are the most popular solutions in literature?
- Are there other strategies than combining small nominal categories to a new, "OTHERS" category?
Please feel free to chime in if you have other suggestions also.