Classification Linear Discriminant Analysis Learner

Minimum Distance Empirical Bayesian classification, designed for small-sample, high-dimensional data. Empirical Bayes estimator where the eigenvalues of the pooled sample covariance matrix are shrunken towards the identity matrix. Calls sparsediscrim::lda_emp_bayes() from FIXME: sparsediscrim.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.mdeb")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, sparsediscrim

Parameters

Id	Type	Default
prior	untyped	NULL

References

Srivastava, M., Kubokawa, T. (2007). “Comparison of Discrimination Methods for High Dimensional Data.” Journal of the Japanese Statistical Association, 37(1), 123–134.

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 -> LearnerClassifMdeb

Methods

Public methods

• LearnerClassifMdeb$new()

• LearnerClassifMdeb$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

LearnerClassifMdeb$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifMdeb$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.mdeb")
print(learner)
#> 
#> ── <LearnerClassifMdeb> (classif.dlda): Minimum Distance Empirical Bayesian Clas
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and sparsediscrim
#> • 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)
#> Minimum Distance Empirical Bayesian Estimator
#> 
#> Sample Size: 139 
#> Number of Features: 60 
#> 
#> Classes and Prior Probabilities:
#>   M (48.92%), R (51.08%) 


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

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