Shrinkage Discriminant Analysis

Shrinkage Discriminant Analysis for classification. Calls sda::sda() from sda.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.sda")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, sda

Parameters

Id	Type	Default	Levels	Range
lambda	numeric	-		\([0, 1]\)
lambda.var	numeric	-		\([0, 1]\)
lambda.freqs	numeric	-		\([0, 1]\)
diagonal	logical	FALSE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-

References

Ahdesmaeki, Miika, Strimmer, Korbinian (2010). “Feature selection in omics prediction problems using cat scores and false nondiscovery rate control.” The Annals of Applied Statistics, 4(1). ISSN 1932-6157, doi:10.1214/09-aoas277 , http://dx.doi.org/10.1214/09-AOAS277.

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 -> LearnerClassifSda

Methods

Public methods

• LearnerClassifSda$new()

• LearnerClassifSda$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifSda$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifSda$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.sda")
print(learner)
#> 
#> ── <LearnerClassifSda> (classif.sda): Shrinkage Discriminant Analysis ──────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and sda
#> • 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)
#> Number of variables: 60 
#> Number of observations: 139 
#> Number of classes: 2 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.0216 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.1134 

print(learner$model)
#> $regularization
#>       lambda   lambda.var lambda.freqs 
#>   0.11341116   0.02160773   1.00000000 
#> 
#> $freqs
#>   M   R 
#> 0.5 0.5 
#> 
#> $alpha
#>         M         R 
#> -4.632688  2.132778 
#> 
#> $beta
#>          V1       V10      V11       V12        V13        V14        V15
#> M  3.469485 -1.063034  2.80197  2.133526  0.5527459 -0.3194863 -0.6405018
#> R -3.469485  1.063034 -2.80197 -2.133526 -0.5527459  0.3194863  0.6405018
#>          V16        V17       V18        V19        V2        V20        V21
#> M  0.1121537 -0.3709685  0.226505  0.7586813  5.933914  0.6161889  0.3845677
#> R -0.1121537  0.3709685 -0.226505 -0.7586813 -5.933914 -0.6161889 -0.3845677
#>           V22        V23        V24        V25         V26        V27       V28
#> M -0.05830444  0.5650316  0.2709981 -0.6245702  0.01207228  0.5366892  0.397712
#> R  0.05830444 -0.5650316 -0.2709981  0.6245702 -0.01207228 -0.5366892 -0.397712
#>           V29       V3       V30       V31        V32        V33      V34
#> M  0.09287878 -7.90592  1.801672 -2.681472  0.1202294  0.9904799 -0.33611
#> R -0.09287878  7.90592 -1.801672  2.681472 -0.1202294 -0.9904799  0.33611
#>          V35       V36       V37        V38       V39       V4       V40
#> M  0.2369105 -1.132062 -1.570107  0.6413819  1.519329  9.45887 -1.495248
#> R -0.2369105  1.132062  1.570107 -0.6413819 -1.519329 -9.45887  1.495248
#>          V41        V42       V43        V44       V45       V46        V47
#> M -0.2435721 -0.9417966  1.253769 -0.2848458  1.221545  1.607202  0.5886535
#> R  0.2435721  0.9417966 -1.253769  0.2848458 -1.221545 -1.607202 -0.5886535
#>        V48       V49        V5       V50       V51       V52       V53
#> M  5.96135  8.966108 -1.392169 -10.25802 -2.547421 -2.340742  1.720018
#> R -5.96135 -8.966108  1.392169  10.25802  2.547421  2.340742 -1.720018
#>         V54       V55       V56      V57        V58       V59         V6
#> M  2.884557 -13.09615 -4.175736 -3.16741  0.4299775  11.76691 -0.2787231
#> R -2.884557  13.09615  4.175736  3.16741 -0.4299775 -11.76691  0.2787231
#>         V60         V7        V8         V9
#> M  1.085997 -0.9606331 -0.190927  0.5412189
#> R -1.085997  0.9606331  0.190927 -0.5412189
#> attr(,"class")
#> [1] "shrinkage"
#> 
#> attr(,"class")
#> [1] "sda"


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
#> Prediction uses 60 features.

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2028986