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 testing dataset). The objective is to ensure that problems such as overfitting (allowing non-inclusive information to influence results) are controlled, as well as provide an insight on how the model will generalize a real problem or on a real...