Statistical FAQs

How are the crossvalidation statistics defined in Partition?

The k-fold crossvalidation method randomly divides all of the (nonexcluded) rows in the datatable, D, into k subsets: D₁, D₂, . . . , D_k. Each row is randomly assigned to one of the k groups.

Note: This means that for k=n (the sample size), some groups will have several rows, and many groups will have none.

K different models are then trained using data from D – D_i, where D_i is the holdout fold. For continuous responses, the error for each observation in D_i is calculated using the model, trained from D – D_i. For nominal and ordinal responses, JMP calculates -2LogLikelihood for each observation in D_i using the model, trained from D-D_i. This is repeated for each of the k folds. The resulting errors are squared and summed (or in the case of nominal and ordinal responses, the -2loglikelihood values are summed) to construct the crossvalidation SSE (or crossvalidation -2loglikelihood in the case of nominal and ordinal responses).

FAQ # 2097
Last Updated: 2005 Jun 30

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