Classification Support Vector Machine Learner

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 the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.smo")
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		-

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.

Author

damirpolat

Super classes

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

Methods

Public methods

• LearnerClassifSMO$new()

• LearnerClassifSMO$clone()

Inherited methods

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

Examples

learner = mlr3::lrn("classif.smo")
print(learner)
#> <LearnerClassifSMO:classif.smo>: Support Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, multiclass, twoclass

# available parameters:
learner$param_set$ids()
#>  [1] "subset"                    "na.action"                
#>  [3] "no_checks"                 "C"                        
#>  [5] "N"                         "L"                        
#>  [7] "P"                         "M"                        
#>  [9] "V"                         "W"                        
#> [11] "K"                         "calibrator"               
#> [13] "E_poly"                    "L_poly"                   
#> [15] "C_poly"                    "C_logistic"               
#> [17] "R_logistic"                "M_logistic"               
#> [19] "output_debug_info"         "do_not_check_capabilities"
#> [21] "num_decimal_places"        "batch_size"               
#> [23] "options"