Random Ferns Classification Learner

Ensemble machine learning algorithm based on Random Ferns, which are a simplified, faster alternative to Random Forests. Calls rFerns::rFerns() from rFerns.

Initial parameter values

• importance:

  • Actual default: FALSE

  • Initial value: "simple"

  • Reason for change: The default value of FALSE will resolve to "none", which turns importance calculation off. To enable importance calculation by default, importance is set to "simple".

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.rFerns")

Meta Information

• Task type: “classif”

• Predict Types: “response”

• Feature Types: “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3extralearners, rFerns

Parameters

Id	Type	Default	Levels	Range
consistentSeed	untyped	NULL		-
depth	integer	5		\([1, 16]\)
ferns	integer	1000		\((-\infty, \infty)\)
importance	untyped	FALSE		-
saveForest	logical	TRUE	TRUE, FALSE	-
threads	integer	0		\((-\infty, \infty)\)

References

Kursa MB (2014). “rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning.” Journal of Statistical Software, 61(10), 1–13. https://www.jstatsoft.org/v61/i10/.

Ozuysal, Mustafa, Calonder, Michael, Lepetit, Vincent, Fua, Pascal (2010). “Fast Keypoint Recognition Using Random Ferns.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 448-461. doi:10.1109/TPAMI.2009.23 .

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 -> LearnerClassifRferns

Methods

Public methods

• LearnerClassifRferns$new()

• LearnerClassifRferns$importance()

• LearnerClassifRferns$oob_error()

• LearnerClassifRferns$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()

---

LearnerClassifRferns$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRferns$new()

---

LearnerClassifRferns$importance()

The importance scores are extracted from the model slot importance.

Usage

LearnerClassifRferns$importance()

Returns

Named numeric().

---

LearnerClassifRferns$oob_error()

OOB error is extracted from the model slot oobErr.

Usage

LearnerClassifRferns$oob_error()

Returns

numeric(1).

---

LearnerClassifRferns$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRferns$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.rFerns")
print(learner)
#> 
#> ── <LearnerClassifRferns> (classif.rFerns): Random Ferns Classifier ────────────
#> • Model: -
#> • Parameters: importance=simple
#> • Packages: mlr3, mlr3extralearners, and rFerns
#> • Predict Types: [response]
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, multiclass, oob_error, 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)

print(learner$model)
#> 
#>  Forest of 1000 ferns of a depth 5.
#> 
#>  OOB error 18.71%; OOB confusion matrix:
#>          True
#> Predicted  M  R
#>         M 65 19
#>         R  7 48
print(learner$importance())
#>           V11           V49           V10            V9           V12 
#>  0.1154899324  0.0798157328  0.0761385473  0.0756659177  0.0615494432 
#>           V51           V47           V48           V36           V21 
#>  0.0571514400  0.0531301121  0.0513046905  0.0459481387  0.0459433466 
#>           V28           V19           V20            V4           V13 
#>  0.0423326233  0.0418634525  0.0406542580  0.0394311932  0.0376933970 
#>           V45           V52           V46           V23           V15 
#>  0.0376837042  0.0356243343  0.0346729231  0.0338117218  0.0327081266 
#>           V35           V31           V22           V37           V27 
#>  0.0319049153  0.0310599737  0.0293807773  0.0291548783  0.0282243193 
#>           V16           V29           V32           V42            V5 
#>  0.0273781724  0.0269716121  0.0268135379  0.0266124252  0.0248911830 
#>           V44           V58           V34            V6           V38 
#>  0.0221212177  0.0220140657  0.0219576527  0.0214080039  0.0210258969 
#>           V39           V14            V2           V25            V8 
#>  0.0199355230  0.0185487377  0.0173994618  0.0173362525  0.0170858205 
#>           V17           V43           V59           V24           V26 
#>  0.0170655980  0.0167548036  0.0157301417  0.0154627913  0.0137067883 
#>           V54           V18           V40            V3           V33 
#>  0.0131125814  0.0129739057  0.0115658566  0.0094688116  0.0092618998 
#>           V55            V1           V56           V41           V53 
#>  0.0081266975  0.0081098586  0.0080874022  0.0078167412  0.0072819316 
#>           V30           V60           V50            V7           V57 
#>  0.0070840185  0.0063483852  0.0059049416 -0.0005975822 -0.0039134527 

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.1594203