I have training data that is classified into 2 categories: X and Not XEach piece of training or experimental data has a variable number of boolean features. Each piece of data may have ~100 features, but the total number of possible boolean features may be in the tens or hundreds of thousands. I want to train a classifier so that given an observation (which has a set of boolean features), it will output the probability that it is X. Could you recommend which ML algorithms could be used for a large number of sparse boolean features?Follow-up question:I also have a large amount of training data that would be best described as Maybe X (I haven't verified is X, but there's a good chance it is X). I could further segment those Maybe X's into Likely X, Maybe X, Probably Not X. Is there a way I can train with Maybe X or it's subclassifications? In the end, what I still only care for is the probability that an observation is X.
Which classifier should I use for sparse boolean features?
machine learning;classification;data mining;data cleaning;supervised learning