Sparse Discriminant Analysis

Sparse Linear Discriminant Analysis for classification. Calls sparseLDA::sda() from sparseLDA.

Custom mlr3 parameters

• Q is set internally to min(n_features, n_classes - 1) when not supplied.

• stop is not exposed because it depends on the task.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.sparseLDA")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, sparseLDA, MASS, elasticnet

Parameters

Id	Type	Default	Levels	Range
lambda	numeric	1e-06		\([0, \infty)\)
maxIte	integer	100		\([0, \infty)\)
tol	numeric	1e-06		\([0, \infty)\)
trace	logical	FALSE	TRUE, FALSE	-

References

Clemmensen, Line, Hastie, Trevor, Witten, Daniela, Ersboll, Bjarne (2011). “Sparse discriminant analysis.” Journal of the American Statistical Association, 106(496), 1519–1531. doi:10.1198/jasa.2011.tm09728 .

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

awinterstetter

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSparseLDA

Methods

Public methods

• LearnerClassifSparseLDA$new()

• LearnerClassifSparseLDA$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

LearnerClassifSparseLDA$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifSparseLDA$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.sparseLDA")
print(learner)
#> 
#> ── <LearnerClassifSparseLDA> (classif.sparseLDA): Sparse Discriminant Analysis ─
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, sparseLDA, MASS, and elasticnet
#> • Predict Types: [response] and prob
#> • Feature Types: integer and numeric
#> • 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:
#> sda.default(x = x, y = y, Q = 1L)
#> 
#> lambda = 1e-06 
#> stop = 60 variables 
#> classes = M, R 
#> 
#> Top 5 predictors (out of 60):
#> 	V6, V21, V45, V11, V41


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.3768116