In earlier chapters, we have explored a range of different models for solving classification and regression problems. It is often found that improved performance can be obtained by combining multiple models together in some way, instead of just using a single model in isolation. For instance, we might train L different models and then make predictions using the average of the predictions made by each model. Such combinations of models are sometimes called committees. In Section 14.2, we discuss ways to apply the committee concept in practice, and we also give some insight into why it can sometimes be an effective procedure.One important variant of the committee method, known as boosting, involves training multiple models in sequence in which the error function used to train a particular model depends on the performance of the previous models. This can produce substantial improvements in performance compared to the use of a single model and is discussed in Section 14.3.
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