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")

Meta Information

, * Task type: “classif”, * Predict Types: “response”, “prob”, * Feature Types: “logical”, “integer”, “numeric”, “factor”, * Required Packages: mlr3, mlr3learners, nnet

Parameters

, |Id |Type |Default |Levels |Range |, |:--------|:---------|:-------|:-----------|:------------------------------------|, |Hess |logical |FALSE |TRUE, FALSE |- |, |abstol |numeric |1e-04 | |\((-\infty, \infty)\) |, |censored |logical |FALSE |TRUE, FALSE |- |, |decay |numeric |0 | |\((-\infty, \infty)\) |, |entropy |logical |FALSE |TRUE, FALSE |- |, |mask |untyped |- | |- |, |maxit |integer |100 | |\([1, \infty)\) |, |MaxNWts |integer |1000 | |\([1, \infty)\) |, |model |logical |FALSE |TRUE, FALSE |- |, |linout |logical |FALSE |TRUE, FALSE |- |, |rang |numeric |0.7 | |\((-\infty, \infty)\) |, |reltol |numeric |1e-08 | |\((-\infty, \infty)\) |, |size |integer |- | |\([1, \infty)\) |, |skip |logical |FALSE |TRUE, FALSE |- |, |softmax |logical |FALSE |TRUE, FALSE |- |, |summ |character |0 |0, 1, 2, 3 |- |, |trace |logical |TRUE |TRUE, FALSE |- |, |Wts |untyped |- | |- |

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.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.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Super classes

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

Methods

Public methods

• LearnerClassifMultinom$new()

• LearnerClassifMultinom$loglik()

• LearnerClassifMultinom$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

LearnerClassifMultinom$new()

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Method loglik()

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

Usage

LearnerClassifMultinom$loglik()

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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", 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 Type: response
#> * 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"