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 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		-

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

Active bindings

marshaled

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

Methods

Public methods

• LearnerClassifSMO$new()

• LearnerClassifSMO$marshal()

• LearnerClassifSMO$unmarshal()

• LearnerClassifSMO$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$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifSMO$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerClassifSMO$marshal(...)

Arguments

...

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

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerClassifSMO$unmarshal(...)

Arguments

...

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

---

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

# Define the Learner
learner = lrn("classif.smo")
print(learner)
#> 
#> ── <LearnerClassifSMO> (classif.smo): Support Vector Machine ───────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and RWeka
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: marshal, missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error'

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

# 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)
#> SMO
#> 
#> Kernel used:
#>   Linear Kernel: K(x,y) = <x,y>
#> 
#> Classifier for classes: M, R
#> 
#> BinarySMO
#> 
#> Machine linear: showing attribute weights, not support vectors.
#> 
#>         -1.2007 * (normalized) V1
#>  +      -0.7307 * (normalized) V10
#>  +      -1.6794 * (normalized) V11
#>  +      -1.5196 * (normalized) V12
#>  +      -0.1552 * (normalized) V13
#>  +       0.4089 * (normalized) V14
#>  +       1.4176 * (normalized) V15
#>  +       0.6036 * (normalized) V16
#>  +      -0.0551 * (normalized) V17
#>  +      -0.2878 * (normalized) V18
#>  +       0.1384 * (normalized) V19
#>  +       0.1258 * (normalized) V2
#>  +      -0.0099 * (normalized) V20
#>  +      -0.6537 * (normalized) V21
#>  +       0.078  * (normalized) V22
#>  +      -0.499  * (normalized) V23
#>  +      -0.833  * (normalized) V24
#>  +      -0.2227 * (normalized) V25
#>  +       0.1225 * (normalized) V26
#>  +      -0.2049 * (normalized) V27
#>  +      -0.47   * (normalized) V28
#>  +       0.152  * (normalized) V29
#>  +       0.5384 * (normalized) V3
#>  +      -0.2885 * (normalized) V30
#>  +       1.0141 * (normalized) V31
#>  +       0.2212 * (normalized) V32
#>  +       0.0854 * (normalized) V33
#>  +       0.2352 * (normalized) V34
#>  +      -0.1081 * (normalized) V35
#>  +       0.9467 * (normalized) V36
#>  +       1.0952 * (normalized) V37
#>  +      -0.4037 * (normalized) V38
#>  +      -0.5591 * (normalized) V39
#>  +      -0.5453 * (normalized) V4
#>  +       0.5122 * (normalized) V40
#>  +      -0.2938 * (normalized) V41
#>  +       0.1581 * (normalized) V42
#>  +      -0.7005 * (normalized) V43
#>  +      -1.0881 * (normalized) V44
#>  +      -0.8227 * (normalized) V45
#>  +      -0.7093 * (normalized) V46
#>  +       0.0194 * (normalized) V47
#>  +      -0.4561 * (normalized) V48
#>  +      -1.2016 * (normalized) V49
#>  +      -0.2306 * (normalized) V5
#>  +       0.7728 * (normalized) V50
#>  +      -1.0212 * (normalized) V51
#>  +      -0.7781 * (normalized) V52
#>  +      -0.67   * (normalized) V53
#>  +      -0.2794 * (normalized) V54
#>  +       0.467  * (normalized) V55
#>  +       0.8367 * (normalized) V56
#>  +       0.0007 * (normalized) V57
#>  +      -0.7605 * (normalized) V58
#>  +      -1.6347 * (normalized) V59
#>  +       0.4291 * (normalized) V6
#>  +       0.0108 * (normalized) V60
#>  +       0.253  * (normalized) V7
#>  +       0.4774 * (normalized) V8
#>  +      -1.0827 * (normalized) V9
#>  +       2.9541
#> 
#> Number of kernel evaluations: 5455 (89.209% cached)
#> 
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2318841