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R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

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Improving model performance with meta-learning

As an alternative to increasing the performance of a single model, it is possible to combine several models to form a powerful team. Just as the best sports teams have players with complementary rather than overlapping skillsets, some of the best machine learning algorithms utilize teams of complementary models. Since a model brings a unique bias to a learning task, it may readily learn one subset of examples, but have trouble with another. Therefore, by intelligently using the talents of several diverse team members, it is possible to create a strong team of multiple weak learners.

This technique of combining and managing the predictions of multiple models falls into a wider set of meta-learning ...

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