nnetOptimisation: nnet parameter optimisation

In lgatto/pRoloc: A unifying bioinformatics framework for spatial proteomics

nnet parameter optimisation

Description

Classification parameter optimisation for artificial neural network algorithm.

Usage

nnetOptimisation(
  object,
  fcol = "markers",
  decay = c(0, 10^(-1:-5)),
  size = seq(1, 10, 2),
  times = 100,
  test.size = 0.2,
  xval = 5,
  fun = mean,
  seed,
  verbose = TRUE,
  ...
)

Arguments

object	An instance of class "MSnSet".
fcol	The feature meta-data containing marker definitions. Default is markers.
decay	The hyper-parameter. Default values are c(0, 10^(-1:-5)).
size	The hyper-parameter. Default values are seq(1, 10, 2).
times	The number of times internal cross-validation is performed. Default is 100.
test.size	The size of test data. Default is 0.2 (20 percent).
xval	The n-cross validation. Default is 5.
fun	The function used to summarise the xval macro F1 matrices.
seed	The optional random number generator seed.
verbose	A logical defining whether a progress bar is displayed.
...	Additional parameters passed to nnet from package nnet.

Details

Note that when performance scores precision, recall and (macro) F1 are calculated, any NA values are replaced by 0. This decision is motivated by the fact that any class that would have either a NA precision or recall would result in an NA F1 score and, eventually, a NA macro F1 (i.e. mean(F1)). Replacing NAs by 0s leads to F1 values of 0 and a reduced yet defined final macro F1 score.

Value

An instance of class "GenRegRes".

Author(s)

Laurent Gatto

See Also

nnetClassification and example therein.

lgatto/pRoloc documentation built on Oct. 23, 2024, 12:51 a.m.