mlr_learners_classif.lda {mlr3learners}

Linear Discriminant Analysis Classification Learner

Description

Linear discriminant analysis. Calls MASS::lda() from package MASS.

Details

Parameters method and prior exist for training and prediction but accept different values for each. Therefore, arguments for the predict stage have been renamed to predict.method and predict.prior, respectively.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.lda")
lrn("classif.lda")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3learners, MASS

Parameters

Id	Type	Default	Levels	Range
dimen	untyped	-		-
method	character	moment	moment, mle, mve, t	-
nu	integer	-		(-\infty, \infty)
predict.method	character	plug-in	plug-in, predictive, debiased	-
predict.prior	untyped	-		-
prior	untyped	-		-
tol	numeric	-		(-\infty, \infty)

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLDA

Methods

Public methods

• LearnerClassifLDA$new()

• LearnerClassifLDA$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifLDA$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifLDA$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Venables WN, Ripley BD (2002). Modern Applied Statistics with S, Fourth edition. Springer, New York. ISBN 0-387-95457-0, http://www.stats.ox.ac.uk/pub/MASS4/.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("MASS", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.lda")
print(learner)

# 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 the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

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

# Score the predictions
predictions$score()
}