Classification Discriminant Analysis Learner

Regularized Discriminant Analysis. Calls klaR::rda() from: klaR.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.rda")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, klaR

Parameters

Id	Type	Default	Levels	Range
lambda	numeric	-		\([0, 1]\)
gamma	numeric	-		\([0, 1]\)
crossval	logical	TRUE	TRUE, FALSE	-
fold	integer	10		\([1, \infty)\)
train.fraction	numeric	0.5		\([0, 1]\)
output	logical	FALSE	TRUE, FALSE	-
schedule	integer	2		\((-\infty, \infty)\)
T.start	numeric	0.1		\([0, \infty)\)
halflife	numeric	50		\([0, \infty)\)
zero.temp	numeric	0.01		\([0, \infty)\)
alpha	numeric	2		\([1, \infty)\)
K	integer	100		\([1, \infty)\)
trafo	logical	TRUE	TRUE, FALSE	-
simAnn	logical	FALSE	TRUE, FALSE	-
estimate.error	logical	TRUE	TRUE, FALSE	-
max.iter	integer	100		\((-\infty, \infty)\)

References

Friedman JH (1989). “Regularized Discriminant Analysis.” Journal of the American Statistical Association, 84(405), 165–175. ISSN 01621459, 1537274X, http://www.jstor.org/stable/2289860.

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 -> LearnerClassifRda

Methods

Public methods

• LearnerClassifRda$new()

• LearnerClassifRda$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRda$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRda$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.rda")
print(learner)
#> 
#> ── <LearnerClassifRda> (classif.rda): Regularized Discriminant Analysis ────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and klaR
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass and twoclass
#> • Other settings: use_weights = 'error'

# 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: 
#> rda(formula = formula, data = data)
#> 
#> Regularization parameters: 
#>     gamma    lambda 
#> 0.2373482 0.7669151 
#> 
#> Prior probabilities of groups: 
#>         M         R 
#> 0.5467626 0.4532374 
#> 
#> Misclassification rate: 
#>        apparent: 5.036 %
#> cross-validated: 19.488 %


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2318841