Classification Stochastic Gradient Descent Learner

Stochastic Gradient Descent for learning various linear models. Calls RWeka::make_Weka_classifier() from RWeka.

Initial parameter values

• F:

  • Has only 2 out of 5 original loss functions: 0 = hinge loss (SVM) and 1 = log loss (logistic regression) with 0 (hinge loss) still being the default

  • Reason for change: this learner should only contain loss functions appropriate for classification tasks

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

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.sgd")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
F	character	0	0, 1	-
L	numeric	0.01		\((-\infty, \infty)\)
R	numeric	1e-04		\((-\infty, \infty)\)
E	integer	500		\((-\infty, \infty)\)
C	numeric	0.001		\((-\infty, \infty)\)
N	logical	-	TRUE, FALSE	-
M	logical	-	TRUE, FALSE	-
S	integer	1		\((-\infty, \infty)\)
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::LearnerClassif -> LearnerClassifSGD

Methods

Public methods

• LearnerClassifSGD$new()

• LearnerClassifSGD$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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

LearnerClassifSGD$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifSGD$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("classif.sgd")
print(learner)
#> <LearnerClassifSGD:classif.sgd>: Stochastic Gradient Descent
#> * Model: -
#> * Parameters: F=0
#> * Packages: mlr3, RWeka
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, twoclass

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

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

print(learner$model)
#> Loss function: Hinge loss (SVM)
#> 
#> Class = 
#> 
#>         -1.1365 (normalized) V1
#>  +       0.3333 (normalized) V10
#>  +      -3.9828 (normalized) V11
#>  +      -2.4191 (normalized) V12
#>  +      -0.3497 (normalized) V13
#>  +       1.6742 (normalized) V14
#>  +       1.0952 (normalized) V15
#>  +       1.7587 (normalized) V16
#>  +       0.8846 (normalized) V17
#>  +       1.5849 (normalized) V18
#>  +      -1.4639 (normalized) V19
#>  +       0.2575 (normalized) V2
#>  +       0.0275 (normalized) V20
#>  +       0.2    (normalized) V21
#>  +      -0.5605 (normalized) V22
#>  +      -2.1056 (normalized) V23
#>  +      -1.801  (normalized) V24
#>  +       2.4229 (normalized) V25
#>  +       1.4998 (normalized) V26
#>  +      -0.4485 (normalized) V27
#>  +      -0.0378 (normalized) V28
#>  +       0.4379 (normalized) V29
#>  +       0.6397 (normalized) V3
#>  +      -1.6145 (normalized) V30
#>  +       3.302  (normalized) V31
#>  +       0.9689 (normalized) V32
#>  +      -0.1147 (normalized) V33
#>  +      -0.1121 (normalized) V34
#>  +       0.2212 (normalized) V35
#>  +       0.1049 (normalized) V36
#>  +       3.759  (normalized) V37
#>  +      -0.1742 (normalized) V38
#>  +      -0.5047 (normalized) V39
#>  +      -0.8963 (normalized) V4
#>  +       2.1648 (normalized) V40
#>  +       0.9668 (normalized) V41
#>  +       0.9151 (normalized) V42
#>  +      -1.6455 (normalized) V43
#>  +      -2.2545 (normalized) V44
#>  +      -2.4498 (normalized) V45
#>  +      -0.6116 (normalized) V46
#>  +       0.688  (normalized) V47
#>  +      -1.9984 (normalized) V48
#>  +      -2.9321 (normalized) V49
#>  +      -1.9259 (normalized) V5
#>  +       1.9642 (normalized) V50
#>  +      -1.5666 (normalized) V51
#>  +      -2.2508 (normalized) V52
#>  +       0.2065 (normalized) V53
#>  +      -0.5824 (normalized) V54
#>  +      -0.0875 (normalized) V55
#>  +      -0.8069 (normalized) V56
#>  +       1.8676 (normalized) V57
#>  +      -1.9369 (normalized) V58
#>  +      -0.0279 (normalized) V59
#>  +       0.2216 (normalized) V6
#>  +       0.5294 (normalized) V60
#>  +       1.3722 (normalized) V7
#>  +       1.3228 (normalized) V8
#>  +      -1.7752 (normalized) V9
#>  +       0.78  


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2608696