Multinomial log-linear learner via neural networks

Multinomial log-linear models via neural networks. Calls nnet::multinom() 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.multinom")
lrn("classif.multinom")

See also

Dictionary of Learners: mlr3::mlr_learners

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifMultinom

Methods

Public methods

• LearnerClassifMultinom$new()

• LearnerClassifMultinom$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

LearnerClassifMultinom$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifMultinom$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
learner$param_set$ids()
}
#> Loading required namespace: nnet
#> <LearnerClassifMultinom:classif.multinom>
#> * Model: -
#> * Parameters: list()
#> * Packages: nnet
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, factor
#> * Properties: multiclass, twoclass, weights
#>  [1] "Hess"     "summ"     "censored" "model"    "rang"     "decay"   
#>  [7] "maxit"    "trace"    "abstol"   "reltol"