Is their any existing Ensemble technique which uses subset of training data to predict which algorithm is better for predicting each instance of testing data?
Let's say we have N sized training set and K sized testing set in which a particular attribute needed to be predicted using the training set. But there are hundreds of algorithms and ways we can use. We can divide training set into two parts and train each model with first half and decide test on second half. Based on characteristics, we can decide which algorithm to use for real test cases (K sized set). As an example lets say dataset have an attribute named "temperature". Particular algorithm may work well when temperature is higher than 100 Celsius. We can then classify all the 100 degree or above instances to particular class. Then final prediction will be done based on that by with that model class trained with all N sized data.
What I am asking is that is their any existing method similar to that?