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): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.0227 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.1058 

print(learner$model)
#> $regularization
#>       lambda   lambda.var lambda.freqs 
#>   0.10579158   0.02268487   1.00000000 
#> 
#> $freqs
#>   M   R 
#> 0.5 0.5 
#> 
#> $alpha
#>         M         R 
#> -4.442131  1.864821 
#> 
#> $beta
#>          V1        V10      V11       V12       V13          V14        V15
#> M  5.981773 -0.9243798  2.72201  2.696162  1.137289 -0.003835789  0.2036479
#> R -5.981773  0.9243798 -2.72201 -2.696162 -1.137289  0.003835789 -0.2036479
#>         V16        V17        V18        V19        V2         V20        V21
#> M -1.130739 -0.3830154  0.6142087  0.9283625  6.330304 -0.02282051 -0.1591195
#> R  1.130739  0.3830154 -0.6142087 -0.9283625 -6.330304  0.02282051  0.1591195
#>          V22       V23        V24        V25        V26        V27         V28
#> M  0.2734222  1.241303  0.5876451 -0.7561536 -0.2575321  0.2063491  0.08686382
#> R -0.2734222 -1.241303 -0.5876451  0.7561536  0.2575321 -0.2063491 -0.08686382
#>           V29        V3      V30       V31         V32        V33         V34
#> M  0.05979306 -13.35534  1.82236 -2.340755 -0.02154636  0.7458659  0.01142841
#> R -0.05979306  13.35534 -1.82236  2.340755  0.02154636 -0.7458659 -0.01142841
#>          V35       V36       V37       V38       V39       V4       V40
#> M  0.2032105 -1.634279 -1.431982  1.531571  1.528567  13.4506 -2.102436
#> R -0.2032105  1.634279  1.431982 -1.531571 -1.528567 -13.4506  2.102436
#>          V41        V42        V43       V44       V45       V46       V47
#> M -0.3780219  0.5049759  0.7703334 -1.338935  1.222275  2.903491  1.985189
#> R  0.3780219 -0.5049759 -0.7703334  1.338935 -1.222275 -2.903491 -1.985189
#>         V48      V49        V5       V50       V51       V52        V53
#> M  4.296285  8.07823 -1.761855 -18.06372 -3.067673  0.426596 -0.5162367
#> R -4.296285 -8.07823  1.761855  18.06372  3.067673 -0.426596  0.5162367
#>         V54      V55      V56       V57       V58       V59        V6       V60
#> M  9.356911 -11.9909 -2.58768 -5.386232 -1.047491  5.042317  1.483546  1.879316
#> R -9.356911  11.9909  2.58768  5.386232  1.047491 -5.042317 -1.483546 -1.879316
#>          V7        V8        V9
#> M -3.279059 -3.937788  2.803834
#> R  3.279059  3.937788 -2.803834
#> 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