Trains and deploys multiple classifiers across a vast parameter search space.
plGridMulti(array.train, array.valid = NULL, ctrlFS, top, how,
aucSkip = FALSE, verbose = TRUE, ...)Specifies the ExprsMulti object to use as training set.
Specifies the ExprsMulti object to use as validation set.
A list of arguments handled by ctrlFeatureSelect.
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.
A character string. Specifies the build method to iterate.
A logical scalar. Argument passed to calcStats.
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.
An ExprsPipeline-class object.
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.
fs
build
doMulti
exprso-predict
plCV
plGrid
plGridMulti
plMonteCarlo
plNested