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): 0.4772 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.0197 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.1083 

print(learner$model)
#> $regularization
#>       lambda   lambda.var lambda.freqs 
#>   0.10829444   0.01967552   0.47720776 
#> 
#> $freqs
#>         M         R 
#> 0.5319693 0.4680307 
#> 
#> $alpha
#>         M         R 
#> -3.306666  1.007997 
#> 
#> $beta
#>          V1        V10       V11       V12       V13        V14        V15
#> M -1.992782  0.4993444  3.198831  2.912087 -1.292663 -0.3198561 -0.2328316
#> R  2.265020 -0.5675609 -3.635829 -3.309913  1.469256  0.3635523  0.2646392
#>         V16        V17        V18        V19        V2        V20         V21
#> M -1.156350 -0.3828564  0.5594978  0.5578774  7.387121 -0.3991425 -0.02639912
#> R  1.314321  0.4351592 -0.6359319 -0.6340902 -8.396291  0.4536702  0.03000556
#>          V22       V23        V24        V25         V26        V27         V28
#> M  0.8628583  1.064837  0.4894980 -0.5708333 -0.03663153 -0.4754514  0.03462023
#> R -0.9807351 -1.210306 -0.5563693  0.6488160  0.04163584  0.5404038 -0.03934977
#>          V29        V3        V30       V31        V32       V33        V34
#> M  0.3516757 -3.900239  0.9207135 -1.347544 -0.2406987 -1.084775 -0.2983859
#> R -0.3997189  4.433059 -1.0464940  1.531635  0.2735811  1.232968  0.3391489
#>          V35       V36       V37        V38       V39        V4       V40
#> M  0.5436684 -1.909503 -1.338888  0.7788254  1.348079  6.820133 -1.352004
#> R -0.6179401  2.170364  1.521796 -0.8852223 -1.532242 -7.751845  1.536704
#>          V41         V42       V43       V44       V45        V46       V47
#> M  0.5958362 -0.02254651  1.606712  2.099012  1.524089  0.7419555  2.425383
#> R -0.6772346  0.02562664 -1.826208 -2.385762 -1.732298 -0.8433156 -2.756719
#>         V48       V49        V5       V50       V51       V52        V53
#> M  4.975737  2.442685 -2.114336 -15.26700 -6.712628 -3.613139  0.8288796
#> R -5.655482 -2.776385  2.403180  17.35266  7.629653  4.106737 -0.9421145
#>        V54       V55       V56       V57       V58       V59        V6
#> M -2.23216 -6.401164  1.383201 -3.468994  7.677517  1.339274  2.921926
#> R  2.53710  7.275640 -1.572163  3.942900 -8.726358 -1.522235 -3.321097
#>           V60        V7        V8        V9
#> M  0.04927809 -2.962471 -1.568713  1.092050
#> R -0.05601007  3.367181  1.783018 -1.241237
#> 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 
#>  0.2173913