Classification Support Vector Machine Learner
mlr_learners_classif.smo.RdSupport 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
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
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.
Super classes
mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSMO
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