Calls nnet::nnet from package nnet.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.nnet")
lrn("classif.nnet")

Traits

  • Packages: nnet

  • Predict Types: prob, response

  • Feature Types: numeric, factor, ordered

  • Properties: multiclass, twoclass, weights

Custom mlr3 defaults

  • size:

    • Adjusted default: 3L

    • Reason for change: no default in nnet().

References

Ripley, B (1996). Pattern Recognition and Neural Networks. Cambridge. http://www.stats.ox.ac.uk/~ripley/PRbook/Compl.pdf

See also

Author

henrifnk

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifNnet

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifNnet$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifNnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("classif.nnet")) print(learner)
#> <LearnerClassifNnet:classif.nnet> #> * Model: - #> * Parameters: size=3 #> * Packages: nnet #> * Predict Type: prob #> * Feature types: numeric, factor, ordered #> * Properties: multiclass, twoclass, weights
# available parameters: learner$param_set$ids()
#> [1] "size" "subset" "na.action" "contrasts" "Wts" "mask" #> [7] "linout" "entropy" "softmax" "censored" "skip" "rang" #> [13] "decay" "maxit" "Hess" "trace" "MaxNWts" "abstol" #> [19] "reltol"