Classification Support Vector Machine Learner
Source:R/learner_RWeka_classif_smo.R
mlr_learners_classif.smo.Rd
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
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
Methods
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 marshal()
Marshal the learner's model.
Arguments
...
(any)
Additional arguments passed tomlr3::marshal_model()
.
Method unmarshal()
Unmarshal the learner's model.
Arguments
...
(any)
Additional arguments passed tomlr3::unmarshal_model()
.
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.
#>
#> -0.743 * (normalized) V1
#> + -0.596 * (normalized) V10
#> + -1.6055 * (normalized) V11
#> + -1.1518 * (normalized) V12
#> + -0.7868 * (normalized) V13
#> + 0.3338 * (normalized) V14
#> + 0.5431 * (normalized) V15
#> + 1.0095 * (normalized) V16
#> + 0.4785 * (normalized) V17
#> + -0.0953 * (normalized) V18
#> + -0.6444 * (normalized) V19
#> + -0.4475 * (normalized) V2
#> + -0.0163 * (normalized) V20
#> + -0.3117 * (normalized) V21
#> + -0.4527 * (normalized) V22
#> + -0.3541 * (normalized) V23
#> + -0.6457 * (normalized) V24
#> + -0.5342 * (normalized) V25
#> + -0.439 * (normalized) V26
#> + 0.0119 * (normalized) V27
#> + -0.1794 * (normalized) V28
#> + -0.3683 * (normalized) V29
#> + 0.5428 * (normalized) V3
#> + -0.4983 * (normalized) V30
#> + 1.0314 * (normalized) V31
#> + -0.3942 * (normalized) V32
#> + -0.1256 * (normalized) V33
#> + 0.3532 * (normalized) V34
#> + 0.3107 * (normalized) V35
#> + 0.8451 * (normalized) V36
#> + 0.4154 * (normalized) V37
#> + -0.4609 * (normalized) V38
#> + -0.2747 * (normalized) V39
#> + -0.4775 * (normalized) V4
#> + 0.6619 * (normalized) V40
#> + -0.1571 * (normalized) V41
#> + -0.1993 * (normalized) V42
#> + -0.4462 * (normalized) V43
#> + -0.5242 * (normalized) V44
#> + -0.7303 * (normalized) V45
#> + -0.1905 * (normalized) V46
#> + 0.1314 * (normalized) V47
#> + -0.4795 * (normalized) V48
#> + -1.482 * (normalized) V49
#> + 0.1161 * (normalized) V5
#> + 0.8031 * (normalized) V50
#> + -0.5486 * (normalized) V51
#> + -1.8556 * (normalized) V52
#> + -0.4095 * (normalized) V53
#> + -0.9304 * (normalized) V54
#> + 1.0989 * (normalized) V55
#> + -0.0927 * (normalized) V56
#> + 0.9633 * (normalized) V57
#> + -0.5128 * (normalized) V58
#> + 0.0261 * (normalized) V59
#> + 0.3446 * (normalized) V6
#> + -0.1695 * (normalized) V60
#> + 0.1464 * (normalized) V7
#> + 0.3076 * (normalized) V8
#> + -1.1118 * (normalized) V9
#> + 4.1518
#>
#> Number of kernel evaluations: 5402 (86.639% cached)
#>
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2028986