Quadratic Discriminant Analysis Classification Learner

Quadratic discriminant analysis. Calls MASS::qda() 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.qda")
lrn("classif.qda")

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

Dictionary of Learners: mlr3::mlr_learners

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifQDA

Methods

Public methods

• LearnerClassifQDA$new()

• LearnerClassifQDA$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifQDA$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifQDA$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("MASS")) {
  learner = mlr3::lrn("classif.qda")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerClassifQDA:classif.qda>
#> * Model: -
#> * Parameters: list()
#> * Packages: MASS
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, factor, ordered
#> * Properties: multiclass, twoclass, weights
#> [1] "prior"          "method"         "nu"             "predict.method"
#> [5] "predict.prior"