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")

Meta Information

• Task type: “classif”

• Predict Types: “prob”, “response”

• Feature Types: “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3learners, nnet

Parameters

Id	Type	Default	Range	Levels
Hess	logical	FALSE	-	TRUE, FALSE
MaxNWts	integer	1000	\([1, \infty)\)	-
Wts	list	-	-	-
abstol	numeric	1e-04	\((-\infty, \infty)\)	-
censored	logical	FALSE	-	TRUE, FALSE
contrasts	list	NULL	-	-
decay	numeric	0	\((-\infty, \infty)\)	-
mask	list	-	-	-
maxit	integer	100	\([1, \infty)\)	-
na.action	list	-	-	-
rang	numeric	0.7	\((-\infty, \infty)\)	-
reltol	numeric	1e-08	\((-\infty, \infty)\)	-
size	integer	3	\([0, \infty)\)	-
skip	logical	FALSE	-	TRUE, FALSE
subset	list	-	-	-
trace	logical	TRUE	-	TRUE, FALSE

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 .

See also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

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.multinom, mlr_learners_classif.naive_bayes, 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.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost, mlr_learners_surv.cv_glmnet, mlr_learners_surv.glmnet, mlr_learners_surv.ranger, mlr_learners_surv.xgboost

Super classes

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

Methods

Public methods

• LearnerClassifNnet$new()

• LearnerClassifNnet$clone()

Inherited methods

• mlr3::Learner$base_learner()

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

  # available parameters:
learner$param_set$ids()
}
#> <LearnerClassifNnet:classif.nnet>
#> * Model: -
#> * Parameters: size=3
#> * Packages: mlr3, mlr3learners, nnet
#> * Predict Type: prob
#> * Feature types: numeric, factor, ordered
#> * Properties: multiclass, twoclass, weights
#>  [1] "Hess"      "MaxNWts"   "Wts"       "abstol"    "censored"  "contrasts"
#>  [7] "decay"     "mask"      "maxit"     "na.action" "rang"      "reltol"   
#> [13] "size"      "skip"      "subset"    "trace"