I have a classification problem, with about 10 different inputs, some boolean, some categorical (and unrelated to each other), some being a float between 0 and 1, which need to be mapped to 4 different outputs.
My problem is that the amount of data that I have is relatively limited. I have about 10,000 data points.
What would make more sense here? Boosted Trees or a Neural Network? I wonder if it makes sense to use a Neural Network at all, given that training an NN seems to require much more data.
Please note I don't want to use SVM, k-means, etc, ideally want to make one of these two methods work.
Also what parameters would you suggest? Like number of trees/leafs? Number of hidden layers? I know a lot of it boils down to experimentation, but what are good/proven starting values to get good results?