Classification Least Squares Support Vector Machine Learner
mlr_learners_classif.lssvm.Rd
Least squares support vector machine for classification.
Calls kernlab::lssvm()
from kernlab.
Parameters sigma
, degree
, scale
, offset
, order
, length
,
lambda
, and normalized
are added to make tuning kpar
easier.
If kpar
is provided then these new parameters are ignored. If none are
provided then the default "automatic" is used for kpar
.
Meta Information
Task type: “classif”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, kernlab
Parameters
Id | Type | Default | Levels | Range |
scaled | untyped | TRUE | - | |
kernel | character | rbfdot | rbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot, stringdot | - |
sigma | numeric | - | \((-\infty, \infty)\) | |
degree | numeric | - | \((-\infty, \infty)\) | |
scale | numeric | - | \((-\infty, \infty)\) | |
offset | numeric | - | \((-\infty, \infty)\) | |
order | numeric | - | \((-\infty, \infty)\) | |
length | integer | - | \([0, \infty)\) | |
lambda | numeric | - | \((-\infty, \infty)\) | |
normalized | logical | - | TRUE, FALSE | - |
kpar | untyped | "automatic" | - | |
tau | numeric | 0.01 | \((-\infty, \infty)\) | |
reduced | logical | TRUE | TRUE, FALSE | - |
rank | integer | - | \((-\infty, \infty)\) | |
delta | integer | 40 | \((-\infty, \infty)\) | |
tol | numeric | 1e-04 | \((-\infty, \infty)\) | |
fit | logical | TRUE | TRUE, FALSE | - |
na.action | untyped | na.omit | - | |
coupler | character | minpair | minpair, pkpd | - |
References
Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11(9), 1–20.
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
-> LearnerClassifLSSVM
Examples
# Define the Learner
learner = mlr3::lrn("classif.lssvm")
print(learner)
#> <LearnerClassifLSSVM:classif.lssvm>: Support Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, kernlab
#> * Predict Types: [response]
#> * Feature Types: integer, numeric
#> * Properties: multiclass, twoclass
# 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)
#> Using automatic sigma estimation (sigest) for RBF or laplace kernel
print(learner$model)
#> Least Squares Support Vector Machine object of class "lssvm"
#>
#> problem type : classification
#> parameter : tau = 0.01
#>
#> Gaussian Radial Basis kernel function.
#> Hyperparameter : sigma = 0.0104611527670791
#>
#> Number of data points used for training : 42
#> Training error : 0.043165
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.08695652