plCV: Perform Simple Cross-Validation

In exprso: Rapid Deployment of Machine Learning Algorithms

Description

Calculates v-fold or leave-one-out cross-validation without selecting a new set of features with each fold. See Details.

Usage

plCV(array, top, how, fold, ...)

Arguments

array	Specifies the ExprsArray object to undergo cross-validation.
top	A numeric scalar or character vector. A numeric scalar indicates the number of top features that should undergo feature selection. A character vector indicates specifically which features by name should undergo feature selection. Set top = 0 to include all features. A numeric vector can also be used to indicate specific features by location, similar to a character vector.
how	A character string. Specifies the build method to iterate.
fold	A numeric scalar. Specifies the number of folds for cross-validation. Set fold = 0 to perform leave-one-out cross-validation.
...	Arguments passed to the how method.

Details

plCV performs v-fold or leave-one-out cross-validation. The argument fold specifies the number of v-folds to use during cross-validation. Set fold = 0 to perform leave-one-out cross-validation. Cross-validation accuracy is defined as the average accuracy from calcStats.

This type of cross-validation is most appropriate if the data has not undergone any prior feature selection. However, it can also serve as an unbiased guide to parameter selection when embedded in plGrid. If using cross-validation in lieu of an independent test set in the setting of one or more feature selection methods, consider using a more "sophisticated" form of cross-validation as implemented in plMonteCarlo or plNested.

When calculating model performance with calcStats, this function forces aucSkip = TRUE and plotSkip = TRUE.

Value

A numeric scalar. The cross-validation accuracy.

exprso documentation built on May 1, 2019, 7:11 p.m.