Skip to contents

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

IdTypeDefaultLevelsRange
subsetuntyped--
na.actionuntyped--
no_checkslogicalFALSETRUE, FALSE-
Cnumeric1\((-\infty, \infty)\)
Ncharacter00, 1, 2-
Lnumeric0.001\((-\infty, \infty)\)
Pnumeric1e-12\((-\infty, \infty)\)
MlogicalFALSETRUE, FALSE-
Vinteger-1\((-\infty, \infty)\)
Winteger1\((-\infty, \infty)\)
KcharacterPolyKernelNormalizedPolyKernel, PolyKernel, Puk, RBFKernel, StringKernel-
calibratoruntyped"weka.classifiers.functions.Logistic"-
E_polynumeric1\((-\infty, \infty)\)
L_polylogicalFALSETRUE, FALSE-
C_polyinteger25007\((-\infty, \infty)\)
C_logisticlogicalFALSETRUE, FALSE-
R_logisticnumeric-\((-\infty, \infty)\)
M_logisticinteger-1\((-\infty, \infty)\)
output_debug_infologicalFALSETRUE, FALSE-
do_not_check_capabilitieslogicalFALSETRUE, FALSE-
num_decimal_placesinteger2\([1, \infty)\)
batch_sizeinteger100\([1, \infty)\)
optionsuntypedNULL-

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

Author

damirpolat

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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 = mlr3::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: missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error'

# 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)
#> SMO
#> 
#> Kernel used:
#>   Linear Kernel: K(x,y) = <x,y>
#> 
#> Classifier for classes: M, R
#> 
#> BinarySMO
#> 
#> Machine linear: showing attribute weights, not support vectors.
#> 
#>         -0.4491 * (normalized) V1
#>  +      -1.7527 * (normalized) V10
#>  +      -1.4174 * (normalized) V11
#>  +      -1.3602 * (normalized) V12
#>  +      -0.8773 * (normalized) V13
#>  +      -0.0641 * (normalized) V14
#>  +       0.54   * (normalized) V15
#>  +       1.0008 * (normalized) V16
#>  +       0.4917 * (normalized) V17
#>  +      -0.5055 * (normalized) V18
#>  +      -0.4056 * (normalized) V19
#>  +       0.0219 * (normalized) V2
#>  +      -0.3727 * (normalized) V20
#>  +      -0.6907 * (normalized) V21
#>  +      -0.7166 * (normalized) V22
#>  +      -0.5016 * (normalized) V23
#>  +      -0.4402 * (normalized) V24
#>  +       0.0765 * (normalized) V25
#>  +       0.637  * (normalized) V26
#>  +      -0.6068 * (normalized) V27
#>  +      -1.2191 * (normalized) V28
#>  +      -0.531  * (normalized) V29
#>  +       0.0872 * (normalized) V3
#>  +      -0.2716 * (normalized) V30
#>  +       1.1417 * (normalized) V31
#>  +       0.5128 * (normalized) V32
#>  +      -0.4475 * (normalized) V33
#>  +      -0.6311 * (normalized) V34
#>  +      -0.5277 * (normalized) V35
#>  +       1.2119 * (normalized) V36
#>  +       1.0594 * (normalized) V37
#>  +      -0.0489 * (normalized) V38
#>  +      -0.3838 * (normalized) V39
#>  +      -0.72   * (normalized) V4
#>  +       0.0043 * (normalized) V40
#>  +      -0.5429 * (normalized) V41
#>  +      -0.316  * (normalized) V42
#>  +      -0.5296 * (normalized) V43
#>  +      -0.5798 * (normalized) V44
#>  +      -0.6464 * (normalized) V45
#>  +      -0.4955 * (normalized) V46
#>  +      -0.8482 * (normalized) V47
#>  +      -0.9119 * (normalized) V48
#>  +      -0.5544 * (normalized) V49
#>  +      -0.61   * (normalized) V5
#>  +       1.0517 * (normalized) V50
#>  +      -0.245  * (normalized) V51
#>  +      -0.4609 * (normalized) V52
#>  +      -0.0576 * (normalized) V53
#>  +      -0.4426 * (normalized) V54
#>  +       0.0653 * (normalized) V55
#>  +      -0.1488 * (normalized) V56
#>  +       0.1436 * (normalized) V57
#>  +       0.2785 * (normalized) V58
#>  +      -0.9417 * (normalized) V59
#>  +       0.1877 * (normalized) V6
#>  +      -0.0832 * (normalized) V60
#>  +       0.7315 * (normalized) V7
#>  +       0.4426 * (normalized) V8
#>  +      -0.6052 * (normalized) V9
#>  +       4.7877
#> 
#> Number of kernel evaluations: 5632 (92.26% cached)
#> 
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2463768