If a categorical feature only occurs a few times in a data set, should I drop it? I have a data set of mostly categorical variables. When I one-hot encoded them some of the features occur less than 3% of the time. 

For instance the Tech-support feature only occurs 928 times in a data set with 32561 samples ie. it only occurs 2.9% of the time.

Is there a general cutoff point for when I should scrap these variables? I'm cleaning up this data set for binary logistic regression and an SVM. 

Thank you!
Indeed it's often a good idea to remove boolean features which are very rare, but the problem is that choosing a threshold by intuition is not necessarily optimal. Whenever possible the optimal value should be determined experimentally, and typically that should be possible for efficient methods such as log regression or SVM. The idea is simply to consider a range of values as threshold and run a grid search using a subset of the training set. The threshold is exactly like an hyper-parameter for the learning method.