nnetOptimisation {pRoloc} | R Documentation

## 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.

[Package pRoloc version 1.24.1 Index]