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 |Levels |Range |, |:---------|:-------|:-------|:-----------|:------------------------------------|, |Hess |logical |FALSE |TRUE, FALSE |- |, |MaxNWts |integer |1000 | |\([1, \infty)\) |, |Wts |untyped |- | |- |, |abstol |numeric |1e-04 | |\((-\infty, \infty)\) |, |censored |logical |FALSE |TRUE, FALSE |- |, |contrasts |untyped | | |- |, |decay |numeric |0 | |\((-\infty, \infty)\) |, |mask |untyped |- | |- |, |maxit |integer |100 | |\([1, \infty)\) |, |na.action |untyped |- | |- |, |rang |numeric |0.7 | |\((-\infty, \infty)\) |, |reltol |numeric |1e-08 | |\((-\infty, \infty)\) |, |size |integer |3 | |\([0, \infty)\) |, |skip |logical |FALSE |TRUE, FALSE |- |, |subset |untyped |- | |- |, |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.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.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"