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/chapters/chapter2/data_and_basic_modeling.html#sec-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', predict_raw = 'FALSE'

# 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): 0.0234 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.1077 

print(learner$model)
#> $regularization
#>       lambda   lambda.var lambda.freqs 
#>   0.10766080   0.02343773   1.00000000 
#> 
#> $freqs
#>   M   R 
#> 0.5 0.5 
#> 
#> $alpha
#>         M         R 
#> -5.723633  2.948352 
#> 
#> $beta
#>          V1       V10       V11       V12       V13        V14       V15
#> M  4.277793  2.984581  2.128093  1.962358  1.854194  0.8094872  0.317756
#> R -4.277793 -2.984581 -2.128093 -1.962358 -1.854194 -0.8094872 -0.317756
#>          V16       V17         V18        V19        V2        V20        V21
#> M -0.5201267 -1.976412 -0.02760296  0.9115067  3.100759  0.7118827  0.3967959
#> R  0.5201267  1.976412  0.02760296 -0.9115067 -3.100759 -0.7118827 -0.3967959
#>          V22        V23        V24        V25        V26         V27       V28
#> M  0.4955305  0.4069361  0.3434566 -0.2301356 -0.2849203  0.08527649  1.264475
#> R -0.4955305 -0.4069361 -0.3434566  0.2301356  0.2849203 -0.08527649 -1.264475
#>           V29        V3        V30       V31       V32         V33       V34
#> M  0.05342988 -8.720105  0.6148855 -1.949805  1.131313  0.02226864 -0.487248
#> R -0.05342988  8.720105 -0.6148855  1.949805 -1.131313 -0.02226864  0.487248
#>         V35       V36      V37        V38        V39        V4       V40
#> M  1.507688 -1.844617 -1.26258  0.8197989  0.4456026  11.04304 -2.602888
#> R -1.507688  1.844617  1.26258 -0.8197989 -0.4456026 -11.04304  2.602888
#>         V41        V42       V43        V44        V45        V46       V47
#> M  1.405053 -0.2620887  0.725286  0.8908467  0.2363866  0.3729708  6.326934
#> R -1.405053  0.2620887 -0.725286 -0.8908467 -0.2363866 -0.3729708 -6.326934
#>         V48       V49         V5       V50       V51       V52       V53
#> M  3.723393  6.492125 -0.3902493 -6.743991 -12.58651 -2.173735  3.648443
#> R -3.723393 -6.492125  0.3902493  6.743991  12.58651  2.173735 -3.648443
#>         V54       V55         V56       V57       V58       V59         V6
#> M  2.587526 -16.94799 -0.06586467 -2.605927  1.858866  5.584141 -0.1463602
#> R -2.587526  16.94799  0.06586467  2.605927 -1.858866 -5.584141  0.1463602
#>         V60        V7        V8        V9
#> M  3.683637 -5.295979 -2.048376  1.484484
#> R -3.683637  5.295979  2.048376 -1.484484
#> 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 
#>  0.3043478