mlr_learners_regr.nnet {mlr3learners}

Neural Network Regression Learner

Description

Single Layer Neural Network. Calls nnet::nnet.formula() from package nnet.

Note that modern neural networks with multiple layers are connected via package mlr3torch.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("regr.nnet")
lrn("regr.nnet")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “integer”, “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	NULL		-
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	-
formula	untyped	-		-

Initial parameter values

• size:

  • Adjusted default: 3L.

  • Reason for change: no default in nnet().

Custom mlr3 parameters

• formula: if not provided, the formula is set to task$formula().

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrNnet

Methods

Public methods

• LearnerRegrNnet$new()

• LearnerRegrNnet$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()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrNnet$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrNnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::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.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

Examples

if (requireNamespace("nnet", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.nnet")
print(learner)

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}