mlr3learners (version 0.5.2)

mlr_learners_classif.lda: Linear Discriminant Analysis Classification Learner

Description

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

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

Super classes

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

Methods

Public methods

• LearnerClassifLDA$new()

• LearnerClassifLDA$clone()

• 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.

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.

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/basics.html#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.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost, mlr_learners_surv.cv_glmnet, mlr_learners_surv.glmnet, mlr_learners_surv.ranger, mlr_learners_surv.xgboost

Examples

# NOT RUN {
if (requireNamespace("MASS", quietly = TRUE)) {
  learner = mlr3::lrn("classif.lda")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
# }