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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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The bias-variance trade-off

The bias-variance trade-off

Figure 8.9: The two extremes of the bias-variance tradeoff:. (left) a (complicated) model with essentially zero bias (on training data) but enormous variance, (right) a simple model with high bias but virtually no variance

In statistical learning, the bias of a model refers to the error of the model introduced by attempting to model a complicated real-life relationship with an approximation. A model with no bias will never make any errors in prediction (like the cookie-area prediction problem). A model with high bias will fail to accurately predict its dependent variable.

The variance of a model refers to how sensitive ...

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