Partial Least Squares Discriminant Analysis Learner

Partial Least Squares Discriminant Analysis for classification. Calls caret::plsda() from caret.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.plsdaCaret")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3extralearners, caret, pls

Parameters

Id	Type	Default	Levels	Range
ncomp	integer	2		\([1, \infty)\)
prior	untyped	"softmax"		-
probMethod	character	softmax	softmax, Bayes	-
method	character	kernelpls	kernelpls, widekernelpls, simpls, oscorespls	-

References

Kuhn, Max (2008). “Building Predictive Models in R Using the caret Package.” Journal of Statistical Software, 28(5), 1–26. doi:10.18637/jss.v028.i05 .

Mevik, Bjorn-Helge, Wehrens, Ron (2007). “The pls Package: Principal Component and Partial Least Squares Regression in R.” Journal of Statistical Software, 18(2), 1–24. doi:10.18637/jss.v018.i02 .

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

awinterstetter

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifPlsdaCaret

Methods

Public methods

• LearnerClassifPlsdaCaret$new()

• LearnerClassifPlsdaCaret$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

LearnerClassifPlsdaCaret$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifPlsdaCaret$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.plsdaCaret")
print(learner)
#> 
#> ── <LearnerClassifPlsdaCaret> (classif.plsdaCaret): Partial Least Squares Discri
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, caret, and pls
#> • Predict Types: [response] and prob
#> • Feature Types: integer and numeric
#> • 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)
#> Partial least squares classification, fitted with the kernel algorithm.
#> The softmax function was used to compute class probabilities.


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2608696