Support vector machine for regression.Callse1071::svm() from packagee1071.
Dictionary
Thismlr3::Learner can be instantiated via thedictionarymlr3::mlr_learners or with the associated sugar functionmlr3::lrn():
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 | 0.1 | \([0, \infty)\) | |
| 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 themlr3book:https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners
Packagemlr3extralearners for more learners.
as.data.table(mlr_learners)for a table of availableLearners 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,mlr3tuningspacesfor 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)){# Define the Learner and set parameter valueslearner=lrn("regr.svm")print(learner)# Define a Tasktask=tsk("mtcars")# Create train and test setids=partition(task)# Train the learner on the training idslearner$train(task, row_ids=ids$train)# print the modelprint(learner$model)# importance methodif("importance"%in%learner$properties)print(learner$importance)# Make predictions for the test rowspredictions=learner$predict(task, row_ids=ids$test)# Score the predictionspredictions$score()}#> <LearnerRegrSVM:regr.svm>: Support Vector Machine#> * Model: -#> * Parameters: list()#> * Packages: mlr3, mlr3learners, e1071#> * Predict Types: [response]#> * Feature Types: logical, integer, numeric#> * Properties: -#>#> Call:#> svm.default(x = data, y = task$truth())#>#>#> Parameters:#> SVM-Type: eps-regression#> SVM-Kernel: radial#> cost: 1#> gamma: 0.1#> epsilon: 0.1#>#>#> Number of Support Vectors: 18#>#> regr.mse#> 14.62665