mlr_learners_classif.smo: Classification Support Vector Machine Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Classification Support Vector Machine Learner

Description

Support Vector classifier trained with John Platt's sequential minimal optimization algorithm. Calls RWeka::SMO() 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

• E_poly:

  • original id: E

• L_poly:

  • original id: L

• C_poly:

  • original id: C

• C_logistic:

  • original id: C

• R_logistic:

  • original id: R

• M_logistic:

  • original id: M

• 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.smo")

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	-		-
no_checks	logical	FALSE	TRUE, FALSE	-
C	numeric	1		(-\infty, \infty)
N	character	0	0, 1, 2	-
L	numeric	0.001		(-\infty, \infty)
P	numeric	1e-12		(-\infty, \infty)
M	logical	FALSE	TRUE, FALSE	-
V	integer	-1		(-\infty, \infty)
W	integer	1		(-\infty, \infty)
K	character	PolyKernel	NormalizedPolyKernel, PolyKernel, Puk, RBFKernel, StringKernel	-
calibrator	untyped	"weka.classifiers.functions.Logistic"		-
E_poly	numeric	1		(-\infty, \infty)
L_poly	logical	FALSE	TRUE, FALSE	-
C_poly	integer	25007		(-\infty, \infty)
C_logistic	logical	FALSE	TRUE, FALSE	-
R_logistic	numeric	-		(-\infty, \infty)
M_logistic	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		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSMO

Methods

Public methods

• LearnerClassifSMO$new()

• LearnerClassifSMO$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

LearnerClassifSMO$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifSMO$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

damirpolat

References

Platt J (1998). “Fast Training of Support Vector Machines using Sequential Minimal Optimization.” In Schoelkopf B, Burges C, Smola A (eds.), Advances in Kernel Methods - Support Vector Learning. MIT Press. http://research.microsoft.com/jplatt/smo.html.

Keerthi S, Shevade S, Bhattacharyya C, Murthy K (2001). “Improvements to Platt's SMO Algorithm for SVM Classifier Design.” Neural Computation, 13(3), 637-649.

Hastie T, Tibshirani R (1998). “Classification by Pairwise Coupling.” In Jordan MI, Kearns MJ, Solla SA (eds.), Advances in Neural Information Processing Systems, volume 10.

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.

Examples

# Define the Learner
learner = mlr3::lrn("classif.smo")
print(learner)

# 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)


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

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.