plGridMulti: Perform High-Throughput Multi-Class Classification

In exprso: Rapid Deployment of Machine Learning Algorithms

Description

Trains and deploys multi-class classifiers across a vast parameter search space.

Usage

plGridMulti(array.train, array.valid = NULL, ctrlFS, top, how,
  aucSkip = FALSE, verbose = TRUE, ...)

Arguments

array.train	Specifies the ExprsMulti object to use as training set.
array.valid	Specifies the ExprsMulti object to use as validation set.
ctrlFS	A list of arguments handled by ctrlFeatureSelect.
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.
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.

Details

Unlike plGrid, the plGridMulti function accepts a ctrlFS argument, allowing for 1-vs-all classification with implicit feature selection. 1-vs-all classification, this function divides the data into 1-vs-all bins, performs a 1-vs-all feature selection for each bin, and then performs a 1-vs-all classification for that same bin. As such, each ExprsMachine within the ExprsModule will have its own unique feature selection history.

Take note, that plGridMulti does not have built-in plCV support. To use plGridMulti with cross-validation, use plNested.

Value

An ExprsPipeline-class object.

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