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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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Linear models are the biggest idea in applied statistics and predictive analytics. There are massive volumes written about the smallest details of linear regression. As such, there are some important ideas that we can't go over here because of space concerns, or because it requires knowledge beyond the scope of this book. So you don't feel like you're in the dark, though, here are some of the topics we didn't cover—and that I would have liked to—and why they are neat.

  • Regularization: Regularization was mentioned briefly in the subsection about balancing bias and variance. In this context, regularization is a technique wherein we penalize models for complexity, to varying degrees. My favorite method of regularizing linear models ...

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