So I've been playing around with SVMs and I wonder if this is a good thing to do:
I have a set of continuous features (0 to 1) and a set of categorical features that I converted to dummy variables. In this particular case, I encode the date of the measurement in a dummy variable:
There are 3 periods that I have data from and I reserved 3 feature numbers for them:
20: 21: 22:
So depending on which period the data comes from, different features will get 1 assigned; the others will get 0.
Will the SVM work properly with this or this is a bad thing to do?
I use SVMLight and a linear kernel.