plGrid: Perform High-Throughput Machine Learning

In tpq/exprso: Rapid Deployment of Machine Learning Algorithms

Description

Trains and deploys models across a vast parameter search space.

Usage

plGrid(array.train, array.valid = NULL, how, top = 0, fold = 10,
  aucSkip = FALSE, plCV.acc = "acc", verbose = FALSE, ...)

Arguments

array.train	The ExprsArray object to use as training set.
array.valid	The ExprsArray object to use as validation set.
how	A character string. The build method to iterate.
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.
fold	A numeric scalar. 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.
plCV.acc	A string. The performance metric to use. For example, choose from "acc", "sens", "spec", "prec", "f1", "auc", or any of the regression specific measures. Argument passed to plCV.
verbose	A logical scalar. Toggles whether to print to console.
...	Arguments passed to the how method. Unlike the build method, plGrid allows multiple parameters for each argument, supplied as a vector. See Details.

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.

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 inner-fold cross-validation via plCV. Use this to select the best model unbiasedly.

Value

An ExprsPipeline-class object.

tpq/exprso documentation built on July 27, 2019, 8:44 a.m.