I'm trying to predict rare events, meaning less than 1% of positive cases. I basically try to predict if a subject will have 0, 1, 2 ... , 6, > 6 failures (there are cases in all those categories).
I've tried several algorithms:
- decision trees
- random forest
- grouping using k-means clustering and finding associations with failures (which group has most failure)
In any case, learning either goes to no failure or has too much variance (leading poor reasults on C.V. set).
Do you know any machine learning algorithms which are better suited for rare events?
Or is it surprising that I get those bad results using those algorithms, which means that my features list is not good?
Thanks a lot.