Classification Logistic Regression Learner

Logistic regression with a quadratic penalization on the coefficient. Calls stepPlr::plr() from stepPlr.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.stepPlr")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, stepPlr

Parameters

Id	Type	Default	Levels	Range
cp	character	aic	aic, bic	-
lambda	numeric	1e-04		\([0, \infty)\)
offset.coefficients	untyped	-		-
offset.subset	untyped	-		-

References

Park, Young M, Hastie, Trevor (2007). “Penalized logistic regression for detecting gene interactions.” Biostatistics, 9(1), 30-50. ISSN 1465-4644, doi:10.1093/biostatistics/kxm010 , https://doi.org/10.1093/biostatistics/kxm010.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• 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.

Author

annanzrv

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifStepPlr

Methods

Public methods

• LearnerClassifStepPlr$new()

• LearnerClassifStepPlr$clone()

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()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifStepPlr$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifStepPlr$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.stepPlr")
print(learner)
#> 
#> ── <LearnerClassifStepPlr> (classif.stepPlr): Logistic Regression with a L2 Pena
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and stepPlr
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: twoclass and weights
#> • Other settings: use_weights = 'use'

# 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)
#> 
#> Call:
#> stepPlr::plr(x = data, y = y)
#> 
#> Coefficients:
#> Intercept        V1       V10       V11       V12       V13       V14       V15 
#>  27.65460 -84.77216   3.59036 -43.76357  -6.49915  -7.54295  17.27831  -7.95019 
#>       V16       V17       V18       V19        V2       V20       V21       V22 
#>  -1.39704  52.58574 -50.97984  20.06954 -35.83129  -6.97767  20.75854 -52.76496 
#>       V23       V24       V25       V26       V27       V28       V29        V3 
#>  27.96769 -48.51728  36.92740 -10.69579   2.32389  -4.69526   0.43245  24.87097 
#>       V30       V31       V32       V33       V34       V35       V36       V37 
#>  -6.08326  35.83491 -19.02138 -19.49399  29.64457 -39.22335  30.66714  15.11764 
#>       V38       V39        V4       V40       V41       V42       V43       V44 
#>  -4.38408 -21.07347 -19.04094  21.82525 -19.50321   8.96858 -14.92662   1.77645 
#>       V45       V46       V47       V48       V49        V5       V50       V51 
#> -10.64085  14.96364  -2.76735 -64.37248 -53.19106  -2.52632  38.72881 -22.38783 
#>       V52       V53       V54       V55       V56       V57       V58       V59 
#> -29.77005 -20.78844 -19.84279  12.41637  -3.44542  -2.67692 -24.98997 -29.24291 
#>        V6       V60        V7        V8        V9 
#>  34.70840 -16.46388   0.39774  46.71265 -25.21659 
#> 
#>     Null deviance: 192.63 on 138 degrees of freedom
#> Residual deviance: 10.45 on 95.43 degrees of freedom
#>             Score: deviance + 4.9 * df = 225.44 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2898551