CHAPTER Ensemble Learning 8 Having studied how to massage the input and calibrate the output, we now turn to techniques for combining different models learned from the data. There are some surprises in store. For example, it is often advantageous to take the train- ing data and derive several different training sets from it, learn a model from each, and combine them to produce an ensemble of learned models. Indeed, techniques for doing this can be very powerful. It is, for example, possible to transform a relatively weak learning scheme into an extremely strong one (in a precise sense that we will explain). Loss of interpretability is a drawback when applying ensemble learning, but there are ways to derive intelligible structured