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/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

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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrMultilayerPerceptron$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerRegrMultilayerPerceptron$marshal(...)

Arguments

...

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

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerRegrMultilayerPerceptron$unmarshal(...)

Arguments

...

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

---

Method 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'

# 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    -0.27019560788326547
#>     Node 1    -0.1718822664325223
#>     Node 2    -1.6189441607724973
#>     Node 3    1.186235355319526
#>     Node 4    1.3907306722676045
#>     Node 5    0.6520216772484777
#> Sigmoid Node 1
#>     Inputs    Weights
#>     Threshold    -0.5396329656113689
#>     Attrib am    0.253360737326545
#>     Attrib carb    0.33436577164843706
#>     Attrib cyl    -0.061773869838566635
#>     Attrib disp    0.15330171404211262
#>     Attrib drat    0.043733948319780655
#>     Attrib gear    0.28949734701107643
#>     Attrib hp    0.2598273055618916
#>     Attrib qsec    -0.07415761847765469
#>     Attrib vs    -0.0606993763985752
#>     Attrib wt    0.22082705867952696
#> Sigmoid Node 2
#>     Inputs    Weights
#>     Threshold    0.7574181386772569
#>     Attrib am    0.4398118081711256
#>     Attrib carb    1.2037770766630689
#>     Attrib cyl    0.06201994702526978
#>     Attrib disp    0.5960277056650897
#>     Attrib drat    0.3998621822993917
#>     Attrib gear    0.7607035151596883
#>     Attrib hp    1.0092615901927822
#>     Attrib qsec    -0.19406176379033768
#>     Attrib vs    0.8135902995802741
#>     Attrib wt    1.1311685905989464
#> Sigmoid Node 3
#>     Inputs    Weights
#>     Threshold    -0.9356546143924589
#>     Attrib am    -0.46263196353594066
#>     Attrib carb    0.04922441497657211
#>     Attrib cyl    -0.26077193949237804
#>     Attrib disp    0.43890145130357006
#>     Attrib drat    0.7344624662405458
#>     Attrib gear    0.3062645757397739
#>     Attrib hp    -0.12738674442334233
#>     Attrib qsec    -0.3372410316612208
#>     Attrib vs    0.6649637236865619
#>     Attrib wt    -1.174324666014897
#> Sigmoid Node 4
#>     Inputs    Weights
#>     Threshold    -1.3098638157247227
#>     Attrib am    0.240939466113163
#>     Attrib carb    0.8777167890634588
#>     Attrib cyl    -0.3346800709385331
#>     Attrib disp    -0.018362777952928896
#>     Attrib drat    0.48428199747603534
#>     Attrib gear    1.2523492202056379
#>     Attrib hp    0.21273766104728464
#>     Attrib qsec    0.004153150704099657
#>     Attrib vs    -0.2740599928717142
#>     Attrib wt    -0.7440110543040349
#> Sigmoid Node 5
#>     Inputs    Weights
#>     Threshold    -0.6501524680161949
#>     Attrib am    -0.03535352716625089
#>     Attrib carb    0.21149132002938612
#>     Attrib cyl    -0.1251805216583407
#>     Attrib disp    0.14266156769795732
#>     Attrib drat    0.6172894612686696
#>     Attrib gear    0.4131021899918383
#>     Attrib hp    -0.08147727456022691
#>     Attrib qsec    -0.21305769449177944
#>     Attrib vs    0.3221656876223614
#>     Attrib wt    -0.6617947345075765
#> 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 
#> 16.90819