Classification Neural Network Learner

Single Layer Neural Network. Calls nnet::nnet.formula() from package nnet.

Note that modern neural networks with multiple layers are connected via package mlr3keras.

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")

Custom mlr3 defaults

• size:

  • Adjusted default: 3L

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

References

Ripley BD (1996). Pattern Recognition and Neural Networks. Cambridge University Press. doi: 10.1017/cbo9780511812651 , https://doi.org/10.1017/cbo9780511812651.

See also

Dictionary of Learners: mlr3::mlr_learners

Super classes

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

Methods

Public methods

• LearnerClassifNnet$new()

• LearnerClassifNnet$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifNnet$new()

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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

if (requireNamespace("nnet")) {
  learner = mlr3::lrn("classif.nnet")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerClassifNnet:classif.nnet>
#> * Model: -
#> * Parameters: size=3
#> * Packages: nnet
#> * Predict Type: prob
#> * Feature types: numeric, factor, ordered
#> * Properties: multiclass, twoclass, weights
#>  [1] "size"      "subset"    "na.action" "contrasts" "Wts"       "mask"     
#>  [7] "linout"    "entropy"   "softmax"   "censored"  "skip"      "rang"     
#> [13] "decay"     "maxit"     "Hess"      "trace"     "MaxNWts"   "abstol"   
#> [19] "reltol"