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Data Analysis with R - Second Edition by Tony Fischetti

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The bias-variance trade-off

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.

Figure 9.9: The two extremes of the bias-variance trade-off: a complicated model with essentially zero bias (on training data) but enormous variance (left), a simple model with high bias but virtually no variance (right)

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

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