Classification Discriminant Analysis Learner

Mixture Discriminant Analysis. Calls mda::mda() from mda.

Initial parameter values

• keep.fitted: Set to FALSE by default for speed.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.mda")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mda

Parameters

Id	Type	Default	Levels	Range
criterion	character	misclassification	misclassification, deviance	-
dimension	integer	-		\([1, \infty)\)
eps	numeric	2.220446e-16		\([0, \infty)\)
iter	integer	5		\([1, \infty)\)
keep.fitted	logical	TRUE	TRUE, FALSE	-
method	character	polyreg	polyreg, mars, bruto, gen.ridge	-
prior	numeric	-		\([0, 1]\)
start.method	character	kmeans	kmeans, lvq	-
sub.df	integer	-		\([1, \infty)\)
subclasses	integer	2		\((-\infty, \infty)\)
tot.df	integer	-		\([1, \infty)\)
trace	logical	FALSE	TRUE, FALSE	-
tries	integer	5		\([1, \infty)\)
weights	untyped	-		-

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

annanzrv

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifMda

Methods

Public methods

• LearnerClassifMda$new()

• LearnerClassifMda$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

LearnerClassifMda$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifMda$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.mda")
print(learner)
#> 
#> ── <LearnerClassifMda> (classif.mda): Mixture Discriminant Analysis ────────────
#> • Model: -
#> • Parameters: keep.fitted=FALSE
#> • Packages: mlr3 and mda
#> • Predict Types: [response] and prob
#> • Feature Types: integer, numeric, factor, and ordered
#> • 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)
#> Call:
#> mda::mda(formula = formula, data = data, keep.fitted = FALSE)
#> 
#> Dimension: 5 
#> 
#> Percent Between-Group Variance Explained:
#>     v1     v2     v3     v4     v5 
#>  49.64  77.45  89.97  96.13 100.00 
#> 
#> Degrees of Freedom (per dimension): 61 
#> 
#> Training Misclassification Error: 0.01439 ( N = 139 )
#> 
#> Deviance: 8.163 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2463768