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): 0.0204 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.1091 

print(learner$model)
#> $regularization
#>       lambda   lambda.var lambda.freqs 
#>   0.10911212   0.02041539   1.00000000 
#> 
#> $freqs
#>   M   R 
#> 0.5 0.5 
#> 
#> $alpha
#>         M         R 
#> -4.310398  1.135969 
#> 
#> $beta
#>          V1       V10       V11       V12       V13       V14        V15
#> M -2.960019  2.537174  4.491107  4.099336  1.638956 -2.728899 -0.8475671
#> R  2.960019 -2.537174 -4.491107 -4.099336 -1.638956  2.728899  0.8475671
#>         V16        V17        V18        V19        V2        V20       V21
#> M -1.755772 -0.4249494  0.3019165  0.1008483 -2.930586  0.7168723  0.804152
#> R  1.755772  0.4249494 -0.3019165 -0.1008483  2.930586 -0.7168723 -0.804152
#>          V22      V23       V24       V25       V26       V27      V28
#> M  0.1109363  1.29756  1.345686 -1.077432 -1.850477 -0.675901  1.33755
#> R -0.1109363 -1.29756 -1.345686  1.077432  1.850477  0.675901 -1.33755
#>         V29        V3       V30        V31        V32        V33       V34
#> M -1.060161 -8.236826  1.816521 -0.9012121  0.1011908 -0.8166067 -1.540803
#> R  1.060161  8.236826 -1.816521  0.9012121 -0.1011908  0.8166067  1.540803
#>           V35       V36        V37       V38       V39        V4       V40
#> M -0.05319385 -1.257508 -0.8193422  1.317181  2.752282  10.56202 -4.240319
#> R  0.05319385  1.257508  0.8193422 -1.317181 -2.752282 -10.56202  4.240319
#>          V41        V42        V43         V44       V45       V46       V47
#> M  0.1154964  0.9414536  0.8546166  0.03839112  1.334726  2.833224  5.882296
#> R -0.1154964 -0.9414536 -0.8546166 -0.03839112 -1.334726 -2.833224 -5.882296
#>         V48       V49        V5      V50      V51      V52      V53       V54
#> M -3.481797  7.268323  6.008035 -18.2585 -3.64491 -2.09687 -2.66903  1.802922
#> R  3.481797 -7.268323 -6.008035  18.2585  3.64491  2.09687  2.66903 -1.802922
#>         V55        V56       V57       V58      V59         V6       V60
#> M -18.12127  0.7300208  2.388298  3.402763  14.5991 -0.7081797 -7.477579
#> R  18.12127 -0.7300208 -2.388298 -3.402763 -14.5991  0.7081797  7.477579
#>          V7        V8        V9
#> M  2.540485 -2.479031  2.165364
#> R -2.540485  2.479031 -2.165364
#> 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