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): 0.3806 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.0251 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.1113 

print(learner$model)
#> $regularization
#>       lambda   lambda.var lambda.freqs 
#>    0.1112586    0.0251449    0.3805853 
#> 
#> $freqs
#>         M         R 
#> 0.5423341 0.4576659 
#> 
#> $alpha
#>         M         R 
#> -4.996040  2.711277 
#> 
#> $beta
#>          V1       V10       V11       V12        V13        V14        V15
#> M  5.591991 -1.363246  4.279834  2.496020  0.9211589 -0.4218282 -0.5748698
#> R -6.626510  1.615447 -5.071604 -2.957784 -1.0915733  0.4998664  0.6812207
#>          V16        V17        V18        V19         V2        V20       V21
#> M -0.9638548 -0.1311606  0.7095271 -0.4580365  0.7037277  0.7383013  0.183319
#> R  1.1421679  0.1554253 -0.8407896  0.5427733 -0.8339174 -0.8748871 -0.217233
#>          V22       V23        V24       V25        V26        V27         V28
#> M -0.3284902  1.598934  0.6036768 -1.202600 -0.4511181  0.7169939  0.08177867
#> R  0.3892609 -1.894737 -0.7153571  1.425081  0.5345750 -0.8496378 -0.09690773
#>         V29        V3       V30       V31        V32        V33        V34
#> M  1.199137 -4.951643  2.246521 -2.638062 -0.3378303  0.1005870 -0.5064155
#> R -1.420977  5.867697 -2.662127  3.126104  0.4003289 -0.1191956  0.6001024
#>             V35       V36       V37        V38       V39         V4       V40
#> M -0.0003632293 -1.378164 -1.557674  0.8101511  1.556051   9.528685 -1.947735
#> R  0.0004304267  1.633124  1.845844 -0.9600291 -1.843921 -11.291492  2.308066
#>          V41       V42        V43        V44       V45       V46        V47
#> M  0.7796085 -1.495069  0.5001277  0.3409253  1.151597  2.758092  0.4062079
#> R -0.9238360  1.771657 -0.5926513 -0.4039965 -1.364643 -3.268339 -0.4813564
#>         V48        V49        V5       V50        V51        V52       V53
#> M  1.940764   8.934196  2.892970 -10.89628 -0.8299130 -0.9095215 -1.179387
#> R -2.299805 -10.587022 -3.428169  12.91209  0.9834469  1.0777830  1.397573
#>         V54       V55       V56       V57       V58       V59        V6
#> M  2.398150 -8.907878 -2.498063 -3.006477  3.865046  6.639299  1.946850
#> R -2.841808 10.555836  2.960204  3.562675 -4.580080 -7.867569 -2.307017
#>          V60         V7        V8        V9
#> M -0.8505313 -0.5993298 -2.475867  2.682181
#> R  1.0078795  0.7102058  2.933902 -3.178385
#> 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 
#>  0.3478261