I have training data with ~600K instances. If I split the training data into four segments and build four separate classifiers, I get much higher accuracy for each model than if I train a single model over the entire data set.
I can do the split for this specific data set because I have prior knowledge of the data. I am wondering if there is an algorithm that will automatically do this task for me.
Given a labelled training data set, divide it into k subsets for k classifiers that are likely to result in higher accuracy.
So something like random forest, but without the "randomness"