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    1.1051040387050717
#>     Node 1    -1.31980971173652
#>     Node 2    -1.8879518062754115
#>     Node 3    1.152000705295494
#>     Node 4    2.07735761654371
#>     Node 5    -0.42818577036863487
#> Sigmoid Node 1
#>     Inputs    Weights
#>     Threshold    -1.1237279845384993
#>     Attrib am    -0.4556436848622621
#>     Attrib carb    1.8997557001478413
#>     Attrib cyl    -0.22779622339987887
#>     Attrib disp    1.1699555275444775
#>     Attrib drat    1.0034367396607193
#>     Attrib gear    1.1333642936265458
#>     Attrib hp    -0.6247871534621207
#>     Attrib qsec    -0.6032708953576351
#>     Attrib vs    -0.056676868245943056
#>     Attrib wt    0.41528392961185584
#> Sigmoid Node 2
#>     Inputs    Weights
#>     Threshold    1.0147275034841623
#>     Attrib am    -0.5435460522518388
#>     Attrib carb    -1.6174001360024803
#>     Attrib cyl    1.12849878951632
#>     Attrib disp    -0.11478574592637611
#>     Attrib drat    -0.7360066723136798
#>     Attrib gear    -0.9945916085657687
#>     Attrib hp    0.4497384333056292
#>     Attrib qsec    -1.5301380909971511
#>     Attrib vs    0.49284718708168906
#>     Attrib wt    0.5153187506599095
#> Sigmoid Node 3
#>     Inputs    Weights
#>     Threshold    -0.8296705052166633
#>     Attrib am    0.32308919284412996
#>     Attrib carb    -0.3039153894330395
#>     Attrib cyl    0.3435278970157961
#>     Attrib disp    0.2719235977239182
#>     Attrib drat    0.31647728488350224
#>     Attrib gear    0.1856130326673884
#>     Attrib hp    1.8594073536474822
#>     Attrib qsec    0.29970657132028417
#>     Attrib vs    0.2512226141482022
#>     Attrib wt    0.0019910187942567856
#> Sigmoid Node 4
#>     Inputs    Weights
#>     Threshold    -0.6372714369277164
#>     Attrib am    0.12640615883443332
#>     Attrib carb    -1.1966033404901983
#>     Attrib cyl    0.289544021711898
#>     Attrib disp    1.0601406828579139
#>     Attrib drat    1.0536145670223447
#>     Attrib gear    0.33502223130537323
#>     Attrib hp    2.858977796341536
#>     Attrib qsec    0.9554175179966133
#>     Attrib vs    0.7556664729146538
#>     Attrib wt    -0.40008818014132363
#> Sigmoid Node 5
#>     Inputs    Weights
#>     Threshold    -0.9058713699078198
#>     Attrib am    0.0011227028673364051
#>     Attrib carb    0.8796917908311987
#>     Attrib cyl    -0.190452787343199
#>     Attrib disp    0.24605794847479878
#>     Attrib drat    0.7363083474527705
#>     Attrib gear    0.6762736887808396
#>     Attrib hp    -0.4715331152117888
#>     Attrib qsec    -0.6645724157089326
#>     Attrib vs    0.07011991574793719
#>     Attrib wt    0.24033239921809352
#> 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 
#> 123.4437