How to get best out of n models?
I've been training text data for classification and have 3 different models for now (SVM / LDA and Bayes) and while all 3 off them give me the same results in average there are some noticeable differences in areas where the model 'doubts' the right label to predict.
So I'd like to combine the actual output of all 3 of them (or even more in future) to come with a kind of 'best out of 3 solutions'
So if all of my models predict label_x for a given record this is an obvious winner
if 2 out of 3 predict label_x this should be the final one
If all 3 predict a different label it needs more attention / be skipped
Are there operators that can do this thing? For now I have a relative complex setup that does this for me also, but if there is something more structured and out of the box available it would be handy.