Principal Component Regression Learner

Principal Component Regression model. Calls pls::pcr() from pls.

Note

During prediction, all components ncomp used in training are used.

Initial parameter values

• model

  • Actual default: TRUE

  • Adjusted default: FALSE

  • Reason for change: keeps model objects compact because predictions do not require the stored model frame.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.pcr")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, mlr3extralearners, pls

Parameters

Id	Type	Default	Levels	Range
ncomp	integer	-		\([1, \infty)\)
method	character	cppls	kernelpls, widekernelpls, simpls, oscorespls, cppls, svdpc	-
scale	logical	FALSE	TRUE, FALSE	-
model	logical	-	TRUE, FALSE	-
x	logical	FALSE	TRUE, FALSE	-
y	logical	FALSE	TRUE, FALSE	-

References

Jolliffe, T. I (1982). “A note on the use of principal components in regression.” Journal of the Royal Statistical Society. Series C (Applied Statistics), 31(3), 300–303.

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

alexander

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrPcr

Methods

Public methods

• LearnerRegrPcr$new()

• LearnerRegrPcr$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$train()
• mlr3::LearnerRegr$predict_newdata_fast()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrPcr$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrPcr$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.pcr")
print(learner)
#> 
#> ── <LearnerRegrPcr> (regr.pcr): Principal Component Regression ─────────────────
#> • Model: -
#> • Parameters: model=FALSE
#> • Packages: mlr3, mlr3extralearners, and pls
#> • Predict Types: [response]
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties:
#> • Other settings: use_weights = 'error'

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

# 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)
#> Principal component regression, fitted with the singular value decomposition algorithm.
#> Call:
#> pcr(formula = formula, data = data, model = FALSE)


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 10.78237