Support vector machine for regression.
Calls e1071::svm()
from package e1071.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.svm")
mlr_learnerslrn("regr.svm")
Meta Information
, * Task type: “regr”, * Predict Types: “response”, * Feature Types: “logical”, “integer”, “numeric”, * Required Packages: mlr3, mlr3learners, e1071
Parameters
, |Id |Type |Default |Levels |Range |, |:---------|:---------|:--------------|:-----------------------------------|:------------------------------------|, |cachesize |numeric |40 | |\((-\infty, \infty)\) |, |coef0 |numeric |0 | |\((-\infty, \infty)\) |, |cost |numeric |1 | |\([0, \infty)\) |, |cross |integer |0 | |\([0, \infty)\) |, |degree |integer |3 | |\([1, \infty)\) |, |epsilon |numeric |- | |\([0, \infty)\) |, |fitted |logical |TRUE |TRUE, FALSE |- |, |gamma |numeric |- | |\([0, \infty)\) |, |kernel |character |radial |linear, polynomial, radial, sigmoid |- |, |nu |numeric |0.5 | |\((-\infty, \infty)\) |, |scale |untyped |TRUE | |- |, |shrinking |logical |TRUE |TRUE, FALSE |- |, |tolerance |numeric |0.001 | |\([0, \infty)\) |, |type |character |eps-regression |eps-regression, nu-regression |- |
References
Cortes, Corinna, Vapnik, Vladimir (1995). “Support-vector networks.” Machine Learning, 20(3), 273--297. doi:10.1007/BF00994018 .
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
Package mlr3extralearners for more learners.
Dictionary of Learners: mlr_learners
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).mlr3pipelines to combine learners with pre- and postprocessing steps.
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Other Learner:
mlr_learners_classif.cv_glmnet
,
mlr_learners_classif.glmnet
,
mlr_learners_classif.kknn
,
mlr_learners_classif.lda
,
mlr_learners_classif.log_reg
,
mlr_learners_classif.multinom
,
mlr_learners_classif.naive_bayes
,
mlr_learners_classif.nnet
,
mlr_learners_classif.qda
,
mlr_learners_classif.ranger
,
mlr_learners_classif.svm
,
mlr_learners_classif.xgboost
,
mlr_learners_regr.cv_glmnet
,
mlr_learners_regr.glmnet
,
mlr_learners_regr.kknn
,
mlr_learners_regr.km
,
mlr_learners_regr.lm
,
mlr_learners_regr.nnet
,
mlr_learners_regr.ranger
,
mlr_learners_regr.xgboost
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrSVM
Examples
if (requireNamespace("e1071", quietly = TRUE)) {
learner = mlr3::lrn("regr.svm")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerRegrSVM:regr.svm>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, e1071
#> * Predict Type: response
#> * Feature types: logical, integer, numeric
#> * Properties: -
#> [1] "cachesize" "coef0" "cost" "cross" "degree" "epsilon"
#> [7] "fitted" "gamma" "kernel" "nu" "scale" "shrinking"
#> [13] "tolerance" "type"