Regression Support Vector Machine Learner

Support Vector Machine for regression. 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

• T_improved:

  • original id: T

• V_improved:

  • original id: V

• P_improved:

  • original id: P

• L_improved:

  • original id: L (duplicated L for when I is set to RegSMOImproved)

• W_improved:

  • original id: W

• C_poly:

  • original id: C

• E_poly:

  • original id: E

• L_poly:

  • original id: L (duplicated L for when K is set to PolyKernel)

• 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("regr.smo_reg")

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	-		-
C	numeric	1		\((-\infty, \infty)\)
N	character	0	0, 1, 2	-
I	character	RegSMOImproved	RegSMO, RegSMOImproved	-
K	character	PolyKernel	NormalizedPolyKernel, PolyKernel, Puk, RBFKernel, StringKernel	-
T_improved	numeric	0.001		\((-\infty, \infty)\)
V_improved	logical	TRUE	TRUE, FALSE	-
P_improved	numeric	1e-12		\((-\infty, \infty)\)
L_improved	numeric	0.001		\((-\infty, \infty)\)
W_improved	integer	1		\((-\infty, \infty)\)
C_poly	integer	250007		\((-\infty, \infty)\)
E_poly	numeric	1		\((-\infty, \infty)\)
L_poly	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		-

References

Shevade S, Keerthi S, Bhattacharyya C, Murthy K (1999). “Improvements to the SMO Algorithm for SVM Regression.” In IEEE Transactions on Neural Networks.

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 -> LearnerRegrSMOreg

Active bindings

marshaled

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

Methods

Public methods

• LearnerRegrSMOreg$new()

• LearnerRegrSMOreg$marshal()

• LearnerRegrSMOreg$unmarshal()

• LearnerRegrSMOreg$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

LearnerRegrSMOreg$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerRegrSMOreg$marshal(...)

Arguments

...

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

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerRegrSMOreg$unmarshal(...)

Arguments

...

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

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrSMOreg$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.smo_reg")
print(learner)
#> 
#> ── <LearnerRegrSMOreg> (regr.smo_reg): Support Vector Machine ──────────────────
#> • 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)
#> SMOreg
#> 
#> weights (not support vectors):
#>  +       0.0301 * (normalized) am
#>  -       0.1701 * (normalized) carb
#>  +       0.1248 * (normalized) cyl
#>  +       0.0385 * (normalized) disp
#>  +       0.37   * (normalized) drat
#>  +       0.0303 * (normalized) gear
#>  -       0.3648 * (normalized) hp
#>  +       0.0615 * (normalized) qsec
#>  -       0.0058 * (normalized) vs
#>  -       0.1998 * (normalized) wt
#>  +       0.4558
#> 
#> 
#> 
#> Number of kernel evaluations: 231 (95.474% cached)


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 17.77637