plGrid: Perform High-Throughput Machine Learning
In exprso: Rapid Deployment of Machine Learning Algorithms

Description Usage Arguments Details Value

Trains and deploys models across a vast parameter search space.

1 2	plGrid(array.train, array.valid = NULL, top, how, fold = 10, aucSkip = FALSE, verbose = FALSE, ...)

`array.train`	Specifies the `ExprsArray` object to use as training set.
`array.valid`	Specifies the `ExprsArray` object to use as validation set.
`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. Note that providing a numeric vector for the `top` argument will have `plGrid` search across multiple top features. However, by providing a list of numeric vectors as the `top` argument, the user can force the default handling of numeric vectors.
`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. Argument passed to `plCV`. Set `fold = NULL` to skip cross-validation altogether.
`aucSkip`	A logical scalar. Argument passed to `calcStats`.
`verbose`	A logical scalar. Argument passed to `exprso-predict`.
`...`	Arguments passed to the `how` method. Unlike the `build` method, `plGrid` allows multiple parameters for each argument, supplied as a vector. See Details.

plGrid will build and exprso-predict for each combination of parameters provided as additional arguments (...). When using plGrid, supplying a numeric vector as the top argument will train and deploy a model of each mentioned size for each combination of parameters provided in .... To skip validation set prediction, use array.valid = NULL. Either way, this function returns an ExprsPipeline-class object which contains a summary of the build parameters and the models themselves. The argument fold controls cross-validation via plCV.