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): 0.615 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.0242 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.1138 

print(learner$model)
#> $regularization
#>       lambda   lambda.var lambda.freqs 
#>   0.11383173   0.02417569   0.61500805 
#> 
#> $freqs
#>         M         R 
#> 0.5207729 0.4792271 
#> 
#> $alpha
#>         M         R 
#> -3.695530  1.232667 
#> 
#> $beta
#>           V1       V10       V11       V12       V13       V14        V15
#> M  0.3075237  1.365294  3.779204  1.966114  1.024047 -1.532948 -0.6554133
#> R -0.3341840 -1.483656 -4.106837 -2.136564 -1.112825  1.665844  0.7122334
#>          V16        V17        V18        V19        V2        V20        V21
#> M -0.7789027 -0.1380161  0.4442139 -0.4096177  1.793072  0.9416295  0.3713993
#> R  0.8464285  0.1499813 -0.4827244  0.4451290 -1.948520 -1.0232627 -0.4035972
#>          V22       V23        V24       V25        V26        V27        V28
#> M  0.1320067  1.058036  0.5754058 -1.686729 -0.1763055  0.2153419  0.6224469
#> R -0.1434508 -1.149761 -0.6252898  1.832958  0.1915901 -0.2340107 -0.6764090
#>          V29        V3        V30       V31        V32        V33        V34
#> M -0.1832268 -10.73540  0.7605486 -2.022411  0.6021126 -0.3641762 -0.3047151
#> R  0.1991114  11.66609 -0.8264832  2.197741 -0.6543118  0.3957480  0.3311319
#>          V35       V36       V37        V38        V39        V4       V40
#> M  0.6045593 -1.487545 -1.578607  0.9075349  0.5409071  10.41976 -2.079081
#> R -0.6569707  1.616506  1.715462 -0.9862123 -0.5878003 -11.32308  2.259324
#>         V41        V42       V43       V44       V45       V46       V47
#> M  1.524142 -0.9899745  1.130474  1.097706  1.124803  2.274324  2.064211
#> R -1.656275  1.0757989 -1.228479 -1.192870 -1.222316 -2.471494 -2.243165
#>          V48        V49        V5       V50       V51       V52       V53
#> M  0.6632397   9.825792  3.008018 -13.87841 -5.886898 -3.420403  3.570291
#> R -0.7207383 -10.677625 -3.268793  15.08158  6.397254  3.716930 -3.879812
#>         V54       V55       V56       V57       V58       V59         V6
#> M  7.591207 -8.593300 -8.123260 -2.793647  2.329945   9.54171  0.2857729
#> R -8.249316  9.338283  8.827494  3.035838 -2.531936 -10.36891 -0.3105475
#>         V60         V7          V8         V9
#> M -8.982838  0.1942529  0.02106854  0.4377749
#> R  9.761592 -0.2110934 -0.02289504 -0.4757272
#> 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 
#>  0.2318841