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Mastering Predictive Analytics with R - Second Edition by Rui Miguel Forte, James D. Miller

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Cross-validation

Cross-validation (which you may hear some data scientists refer to as rotation estimation, or simply a general technique for assessing models), is another method for assessing a model's performance (or its accuracy).

Mainly used with predictive modeling to estimate how accurately a model might perform in practice, one might see cross-validation used to check how a model will potentially generalize; in other words, how the model will apply what it infers from samples, to an entire population (or dataset).

With cross-validation, you identify a (known) dataset as your validation dataset on which training is run, along with a dataset of unknown data (or first seen data) against which the model will be tested (this is known as your ...

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