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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")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • Feature Types: “logical”, “integer”, “numeric”, “factor”

  • Required Packages: mlr3, mlr3learners, nnet

Parameters

IdTypeDefaultLevelsRange
HesslogicalFALSETRUE, FALSE-
abstolnumeric1e-04\((-\infty, \infty)\)
censoredlogicalFALSETRUE, FALSE-
decaynumeric0\((-\infty, \infty)\)
entropylogicalFALSETRUE, FALSE-
maskuntyped--
maxitinteger100\([1, \infty)\)
MaxNWtsinteger1000\([1, \infty)\)
modellogicalFALSETRUE, FALSE-
linoutlogicalFALSETRUE, FALSE-
rangnumeric0.7\((-\infty, \infty)\)
reltolnumeric1e-08\((-\infty, \infty)\)
sizeinteger-\([1, \infty)\)
skiplogicalFALSETRUE, FALSE-
softmaxlogicalFALSETRUE, FALSE-
summcharacter00, 1, 2, 3-
tracelogicalTRUETRUE, FALSE-
Wtsuntyped--

See also

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method loglik()

Extract the log-likelihood (e.g., via stats::logLik() from the fitted model.

Usage

LearnerClassifMultinom$loglik()


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", quietly = TRUE)) {
  learner = mlr3::lrn("classif.multinom")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerClassifMultinom:classif.multinom>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, nnet
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: loglik, multiclass, twoclass, weights
#>  [1] "Hess"     "abstol"   "censored" "decay"    "entropy"  "mask"    
#>  [7] "maxit"    "MaxNWts"  "model"    "linout"   "rang"     "reltol"  
#> [13] "size"     "skip"     "softmax"  "summ"     "trace"    "Wts"