Classification Boosting Learner

Classification using ada. Calls ada::ada() from ada.

Initial parameter values

• xval is set to 0 by default for speed.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.ada")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3extralearners, ada, rpart

Parameters

Id	Type	Default	Levels	Range
bag.frac	numeric	0.5		\([0, 1]\)
bag.shift	logical	FALSE	TRUE, FALSE	-
cp	numeric	0.01		\([0, 1]\)
delta	numeric	1e-10		\([0, \infty)\)
iter	integer	50		\([1, \infty)\)
loss	character	exponential	exponential, logistic	-
max.iter	integer	20		\([1, \infty)\)
maxcompete	integer	4		\([0, \infty)\)
maxdepth	integer	30		\([1, 30]\)
maxsurrogate	integer	5		\([0, \infty)\)
minbucket	integer	-		\([1, \infty)\)
minsplit	integer	20		\([1, \infty)\)
model.coef	logical	TRUE	TRUE, FALSE	-
n.iter	integer	50		\([1, \infty)\)
nu	numeric	0.1		\([0, \infty)\)
surrogatestyle	integer	0		\([0, 1]\)
type	character	discrete	discrete, real, gentle	-
usesurrogate	integer	2		\([0, 2]\)
verbose	logical	FALSE	TRUE, FALSE	-
xval	integer	0		\([0, \infty)\)

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 -> LearnerClassifAdaBoosting

Methods

Public methods

• LearnerClassifAdaBoosting$new()

• LearnerClassifAdaBoosting$oob_error()

• LearnerClassifAdaBoosting$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

LearnerClassifAdaBoosting$new()

---

Method oob_error()

The oob error is extracted extracted from the model slot $model$errs.

Usage

LearnerClassifAdaBoosting$oob_error()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifAdaBoosting$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.ada")
print(learner)
#> 
#> ── <LearnerClassifAdaBoosting> (classif.ada): ada Boosting ─────────────────────
#> • Model: -
#> • Parameters: xval=0
#> • Packages: mlr3, mlr3extralearners, ada, and rpart
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: missings, oob_error, 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)

print(learner$model)
#> Call:
#> ada(task$formula(), data = task$data(), control = list(xval = 0L))
#> 
#> Loss: exponential Method: discrete   Iteration: 50 
#> 
#> Final Confusion Matrix for Data:
#>           Final Prediction
#> True value  M  R
#>          M 76  1
#>          R  3 59
#> 
#> Train Error: 0.029 
#> 
#> Out-Of-Bag Error:  0.043  iteration= 50 
#> 
#> Additional Estimates of number of iterations:
#> 
#> train.err1 train.kap1 
#>         39         39 
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.1449275