Regression MultilayerPerceptron Learner

Regressor that uses backpropagation to learn a multi-layer perceptron. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

• output_debug_info:

  • original id: output-debug-info

• do_not_check_capabilities:

  • original id: do-not-check-capabilities

• num_decimal_places:

  • original id: num-decimal-places

• batch_size:

  • original id: batch-size

• Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

• G removed:

  • GUI will be opened

• Reason for change: The parameter is removed because we don't want to launch GUI.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.multilayer_perceptron")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
L	numeric	0.3		\([0, 1]\)
M	numeric	0.2		\([0, 1]\)
N	integer	500		\([1, \infty)\)
V	numeric	0		\([0, 100]\)
S	integer	0		\([0, \infty)\)
E	integer	20		\([1, \infty)\)
A	logical	FALSE	TRUE, FALSE	-
B	logical	FALSE	TRUE, FALSE	-
H	untyped	"a"		-
C	logical	FALSE	TRUE, FALSE	-
I	logical	FALSE	TRUE, FALSE	-
R	logical	FALSE	TRUE, FALSE	-
D	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		\([1, \infty)\)
batch_size	integer	100		\([1, \infty)\)
options	untyped	NULL		-

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/chapters/chapter2/data_and_basic_modeling.html#sec-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

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrMultilayerPerceptron

Active bindings

marshaled

(logical(1))
Whether the learner has been marshaled.

Methods

Public methods

• LearnerRegrMultilayerPerceptron$new()

• LearnerRegrMultilayerPerceptron$marshal()

• LearnerRegrMultilayerPerceptron$unmarshal()

• LearnerRegrMultilayerPerceptron$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()

---

LearnerRegrMultilayerPerceptron$new()

Creates a new instance of this R6 class.

Usage

LearnerRegrMultilayerPerceptron$new()

---

LearnerRegrMultilayerPerceptron$marshal()

Marshal the learner's model.

Usage

LearnerRegrMultilayerPerceptron$marshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::marshal_model().

---

LearnerRegrMultilayerPerceptron$unmarshal()

Unmarshal the learner's model.

Usage

LearnerRegrMultilayerPerceptron$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::unmarshal_model().

---

LearnerRegrMultilayerPerceptron$clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrMultilayerPerceptron$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.multilayer_perceptron")
print(learner)
#> 
#> ── <LearnerRegrMultilayerPerceptron> (regr.multilayer_perceptron): MultilayerPer
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and RWeka
#> • Predict Types: [response]
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: marshal and missings
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'

# 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(learner$model)
#> Linear Node 0
#>     Inputs    Weights
#>     Threshold    0.6945753705353622
#>     Node 1    -0.8857573393066372
#>     Node 2    -1.1288375332827605
#>     Node 3    2.2484648097610074
#>     Node 4    0.15305929766570203
#>     Node 5    -0.5419214222784402
#> Sigmoid Node 1
#>     Inputs    Weights
#>     Threshold    -0.6729927509735206
#>     Attrib am    0.22825082622934723
#>     Attrib carb    0.6217850427763009
#>     Attrib cyl    -0.9683924869153384
#>     Attrib disp    0.11098472509440817
#>     Attrib drat    -0.006211296668445627
#>     Attrib gear    0.8527427853978174
#>     Attrib hp    0.06889619785390859
#>     Attrib qsec    -0.2795234416221884
#>     Attrib vs    0.4285116343702588
#>     Attrib wt    0.6534516672352739
#> Sigmoid Node 2
#>     Inputs    Weights
#>     Threshold    0.9704109464504828
#>     Attrib am    0.13309470695217804
#>     Attrib carb    -0.4236557113486706
#>     Attrib cyl    -0.24615703182310644
#>     Attrib disp    -0.771583748304646
#>     Attrib drat    -1.1311364512619775
#>     Attrib gear    0.17607526549204253
#>     Attrib hp    0.09265738327274287
#>     Attrib qsec    -0.5452935407236874
#>     Attrib vs    -1.7708169683791184
#>     Attrib wt    1.996798011294549
#> Sigmoid Node 3
#>     Inputs    Weights
#>     Threshold    -2.3087471488834908
#>     Attrib am    1.5771475830246093
#>     Attrib carb    -0.033008036176196354
#>     Attrib cyl    0.8250178139460521
#>     Attrib disp    -0.6339543674410977
#>     Attrib drat    -0.35688401727222613
#>     Attrib gear    1.0239487112537013
#>     Attrib hp    0.10170526549723792
#>     Attrib qsec    2.6224849739758986
#>     Attrib vs    -0.6889210235731933
#>     Attrib wt    -1.2457175172573987
#> Sigmoid Node 4
#>     Inputs    Weights
#>     Threshold    -0.9805472982670688
#>     Attrib am    0.1150678344279488
#>     Attrib carb    0.6269991731457484
#>     Attrib cyl    0.10463748965618738
#>     Attrib disp    0.034465288200853116
#>     Attrib drat    0.22707795981557985
#>     Attrib gear    0.3848741833125485
#>     Attrib hp    0.4910747123090154
#>     Attrib qsec    0.31094301735448426
#>     Attrib vs    -0.06572258232130679
#>     Attrib wt    -0.03967231281563914
#> Sigmoid Node 5
#>     Inputs    Weights
#>     Threshold    -0.6648502557697633
#>     Attrib am    0.3306708141058065
#>     Attrib carb    0.6054257684847401
#>     Attrib cyl    -0.6067687337679653
#>     Attrib disp    0.2225650659004779
#>     Attrib drat    0.12699226201619315
#>     Attrib gear    0.7289918120667643
#>     Attrib hp    0.15942887640657538
#>     Attrib qsec    -0.2279605833709971
#>     Attrib vs    0.27334158039310075
#>     Attrib wt    0.4951522908044466
#> Class 
#>     Input
#>     Node 0
#> 


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

# Score the predictions
predictions$score()
#> regr.mse 
#>   10.798