Regression Neural Network Learner

Bayesian regularization for feed-forward neural networks. Calls brnn::brnn from brnn.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.brnn")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3extralearners, brnn

Parameters

Id	Type	Default	Levels	Range
change	numeric	0.001		\((-\infty, \infty)\)
cores	integer	1		\([1, \infty)\)
epochs	integer	1000		\([1, \infty)\)
min_grad	numeric	1e-10		\((-\infty, \infty)\)
Monte_Carlo	logical	FALSE	TRUE, FALSE	-
mu	numeric	0.005		\((-\infty, \infty)\)
mu_dec	numeric	0.1		\((-\infty, \infty)\)
mu_inc	numeric	10		\((-\infty, \infty)\)
mu_max	numeric	1e+10		\((-\infty, \infty)\)
neurons	integer	2		\([1, \infty)\)
normalize	logical	TRUE	TRUE, FALSE	-
samples	integer	40		\([1, \infty)\)
tol	numeric	1e-06		\((-\infty, \infty)\)
verbose	logical	FALSE	TRUE, FALSE	-

See also

• 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).

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

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

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

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

Author

annanzrv

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrBrnn

Methods

Public methods

• LearnerRegrBrnn$new()

• LearnerRegrBrnn$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerRegr$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrBrnn$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrBrnn$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.brnn")
print(learner)
#> 
#> ── <LearnerRegrBrnn> (regr.brnn): Bayesian regularization for feed-forward neura
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, and brnn
#> • Predict Types: [response]
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties:
#> • Other settings: use_weights = 'error'

# 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)
#> Number of parameters (weights and biases) to estimate: 24 
#> Nguyen-Widrow method
#> Scaling factor= 0.7234904 
#> gamma= 7.5563 	 alpha= 3.0945 	 beta= 10.7761 

print(learner$model)
#> A Bayesian regularized neural network 
#> 10 - 2 - 1 with 24 weights, biases and connection strengths
#> Inputs and output were  normalized
#> Training finished because  Changes in F= beta*SCE + alpha*Ew in last 3 iterations less than 0.001 


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 8.801579