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Support Vector Machine for regression. Calls RWeka::make_Weka_classifier() 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

  • T_improved:

    • original id: T

  • V_improved:

    • original id: V

  • P_improved:

    • original id: P

  • L_improved:

    • original id: L (duplicated L for when I is set to RegSMOImproved)

  • W_improved:

    • original id: W

  • C_poly:

    • original id: C

  • E_poly:

    • original id: E

  • L_poly:

    • original id: L (duplicated L for when K is set to PolyKernel)

  • Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.smo_reg")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

  • Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

  • Required Packages: mlr3, RWeka

Parameters

IdTypeDefaultLevelsRange
subsetuntyped--
na.actionuntyped--
Cnumeric1\((-\infty, \infty)\)
Ncharacter00, 1, 2-
IcharacterRegSMOImprovedRegSMO, RegSMOImproved-
KcharacterPolyKernelNormalizedPolyKernel, PolyKernel, Puk, RBFKernel, StringKernel-
T_improvednumeric0.001\((-\infty, \infty)\)
V_improvedlogicalTRUETRUE, FALSE-
P_improvednumeric1e-12\((-\infty, \infty)\)
L_improvednumeric0.001\((-\infty, \infty)\)
W_improvedinteger1\((-\infty, \infty)\)
C_polyinteger250007\((-\infty, \infty)\)
E_polynumeric1\((-\infty, \infty)\)
L_polylogicalFALSETRUE, FALSE-
output_debug_infologicalFALSETRUE, FALSE-
do_not_check_capabilitieslogicalFALSETRUE, FALSE-
num_decimal_placesinteger2\([1, \infty)\)
batch_sizeinteger100\([1, \infty)\)
optionsuntypedNULL-

References

Shevade S, Keerthi S, Bhattacharyya C, Murthy K (1999). “Improvements to the SMO Algorithm for SVM Regression.” In IEEE Transactions on Neural Networks.

See also

Author

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrSMOreg

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrSMOreg$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("regr.smo_reg")
print(learner)
#> <LearnerRegrSMOreg:regr.smo_reg>: Support Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types:  [response]
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings

# Define a Task
task = mlr3::tsk("mtcars")

# 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)
#> SMOreg
#> 
#> weights (not support vectors):
#>  +       0.045  * (normalized) am
#>  -       0.1246 * (normalized) carb
#>  -       0.148  * (normalized) cyl
#>  -       0.13   * (normalized) disp
#>  +       0.0066 * (normalized) drat
#>  +       0.029  * (normalized) gear
#>  -       0.1227 * (normalized) hp
#>  -       0.021  * (normalized) qsec
#>  +       0.0128 * (normalized) vs
#>  -       0.2329 * (normalized) wt
#>  +       0.6843
#> 
#> 
#> 
#> Number of kernel evaluations: 231 (97.415% cached)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> regr.mse 
#> 8.955905