Cross-Validation

Cross-Validation (Statistical Model Validation) (GWU EMSE-271)
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 * "Cross-validation**, sometimes called **rotation estimation**, is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice.

"One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the //training set//), and validating the analysis on the other subset (called the //validation set// or //testing set//). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds. Cross-validation is important in guarding against testing hypotheses suggested by the data (called "Type III errors"]), especially where further samples are hazardous, costly or impossible to collect" - [|Wikipedia]

NOTE: Still want to read rest of article and compare to Lattin. Page TBD.


 * Sources:**
 * Cross-validation (statistics). (2009, November 27). In //Wikipedia, The Free Encyclopedia//. Retrieved 14:45, December 8, 2009, from []
 * EMSE 271, Fall 2009