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What are the ensemble models?

Ensemble methods are about combining multiple machine learning models to take advantage of their diversity of strengths, to get better results by integrating all of their predictions. There is a diversity of methods for how to combine multiple learners. Some of them combine their results through different types of averaging, as was the case in our small example. Others combine the models sequentially so that each successive model aims to boost the accuracy of the previous models by focusing on the datapoints where they make errors. Still others build hierarchies of models where models on a higher level learn how to combine the predictions of the lower level models to get better accuracy.