Classification AdaBoostM1 Learner

Adaptive boosting algorithm for classification. Calls RWeka::AdaBoostM1() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.AdaBoostM1")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
P	integer	100		\([90, 100]\)
Q	logical	FALSE	TRUE, FALSE	-
S	integer	1		\([1, \infty)\)
I	integer	10		\([1, \infty)\)
W	untyped	"DecisionStump"		-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		\([1, \infty)\)
batch_size	integer	100		\([1, \infty)\)
options	untyped	NULL		-

Parameter changes

• output_debug_info:

  • original id: output-debug-info

• do_not_check_capabilities:

  • original id: do-not-check-capabilities

• num_decimal_places:

  • original id: num-decimal-places

• batch_size:

  • original id: batch-size

• Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

References

Freund, Yoav, Schapire, E R, others (1996). “Experiments with a new boosting algorithm.” In icml, volume 96, 148–156. Citeseer.

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

henrifnk

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifAdaBoostM1

Active bindings

marshaled

(logical(1))
Whether the learner has been marshaled.

Methods

Public methods

• LearnerClassifAdaBoostM1$new()

• LearnerClassifAdaBoostM1$marshal()

• LearnerClassifAdaBoostM1$unmarshal()

• LearnerClassifAdaBoostM1$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

LearnerClassifAdaBoostM1$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerClassifAdaBoostM1$marshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::marshal_model().

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerClassifAdaBoostM1$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::unmarshal_model().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifAdaBoostM1$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.AdaBoostM1")
print(learner)
#> 
#> ── <LearnerClassifAdaBoostM1> (classif.AdaBoostM1): Adaptive Boosting ──────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, and RWeka
#> • Predict Types: [response] and prob
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: marshal, 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)

print(learner$model)
#> AdaBoostM1: Base classifiers and their weights: 
#> 
#> Decision Stump
#> 
#> Classifications
#> 
#> V11 <= 0.17070000000000002 : R
#> V11 > 0.17070000000000002 : M
#> V11 is missing : R
#> 
#> Class distributions
#> 
#> V11 <= 0.17070000000000002
#> M	R	
#> 0.12727272727272726	0.8727272727272727	
#> V11 > 0.17070000000000002
#> M	R	
#> 0.7380952380952381	0.2619047619047619	
#> V11 is missing
#> M	R	
#> 0.49640287769784175	0.5035971223021583	
#> 
#> 
#> Weight: 1.33
#> 
#> Decision Stump
#> 
#> Classifications
#> 
#> V49 <= 0.0238 : R
#> V49 > 0.0238 : M
#> V49 is missing : R
#> 
#> Class distributions
#> 
#> V49 <= 0.0238
#> M	R	
#> 0.054409005628517824	0.9455909943714821	
#> V49 > 0.0238
#> M	R	
#> 0.5189290943317297	0.4810709056682702	
#> V49 is missing
#> M	R	
#> 0.40250783699059584	0.5974921630094042	
#> 
#> 
#> Weight: 0.51
#> 
#> Decision Stump
#> 
#> Classifications
#> 
#> V45 <= 0.32375 : R
#> V45 > 0.32375 : M
#> V45 is missing : R
#> 
#> Class distributions
#> 
#> V45 <= 0.32375
#> M	R	
#> 0.2518639733335287	0.7481360266664713	
#> V45 > 0.32375
#> M	R	
#> 0.8346481787898782	0.16535182121012187	
#> V45 is missing
#> M	R	
#> 0.328892381944902	0.671107618055098	
#> 
#> 
#> Weight: 1.15
#> 
#> Decision Stump
#> 
#> Classifications
#> 
#> V37 <= 0.45465 : M
#> V37 > 0.45465 : R
#> V37 is missing : M
#> 
#> Class distributions
#> 
#> V37 <= 0.45465
#> M	R	
#> 0.6887887667411455	0.31121123325885447	
#> V37 > 0.45465
#> M	R	
#> 0.1763658609450729	0.8236341390549271	
#> V37 is missing
#> M	R	
#> 0.5271686250118222	0.4728313749881778	
#> 
#> 
#> Weight: 1.0
#> 
#> Decision Stump
#> 
#> Classifications
#> 
#> V28 <= 0.9233 : R
#> V28 > 0.9233 : M
#> V28 is missing : R
#> 
#> Class distributions
#> 
#> V28 <= 0.9233
#> M	R	
#> 0.3555358063832327	0.6444641936167673	
#> V28 > 0.9233
#> M	R	
#> 0.8760000580301067	0.12399994196989325	
#> V28 is missing
#> M	R	
#> 0.4259092173898291	0.574090782610171	
#> 
#> 
#> Weight: 0.73
#> 
#> Decision Stump
#> 
#> Classifications
#> 
#> V21 <= 0.53485 : R
#> V21 > 0.53485 : M
#> V21 is missing : M
#> 
#> Class distributions
#> 
#> V21 <= 0.53485
#> M	R	
#> 0.265746902435134	0.7342530975648661	
#> V21 > 0.53485
#> M	R	
#> 0.7145241413888114	0.2854758586111887	
#> V21 is missing
#> M	R	
#> 0.5617822463633587	0.4382177536366412	
#> 
#> 
#> Weight: 0.95
#> 
#> Decision Stump
#> 
#> Classifications
#> 
#> V42 <= 0.27454999999999996 : R
#> V42 > 0.27454999999999996 : M
#> V42 is missing : R
#> 
#> Class distributions
#> 
#> V42 <= 0.27454999999999996
#> M	R	
#> 0.31736145057646625	0.6826385494235337	
#> V42 > 0.27454999999999996
#> M	R	
#> 0.7516036376180585	0.24839636238194163	
#> V42 is missing
#> M	R	
#> 0.4889845330712027	0.5110154669287973	
#> 
#> 
#> Weight: 0.89
#> 
#> Decision Stump
#> 
#> Classifications
#> 
#> V51 <= 0.0129 : R
#> V51 > 0.0129 : M
#> V51 is missing : M
#> 
#> Class distributions
#> 
#> V51 <= 0.0129
#> M	R	
#> 0.3427097254871734	0.6572902745128266	
#> V51 > 0.0129
#> M	R	
#> 0.7136968307313384	0.28630316926866156	
#> V51 is missing
#> M	R	
#> 0.5400210207298772	0.4599789792701228	
#> 
#> 
#> Weight: 0.79
#> 
#> Decision Stump
#> 
#> Classifications
#> 
#> V59 <= 0.01095 : R
#> V59 > 0.01095 : M
#> V59 is missing : M
#> 
#> Class distributions
#> 
#> V59 <= 0.01095
#> M	R	
#> 0.41614983507323616	0.5838501649267639	
#> V59 > 0.01095
#> M	R	
#> 0.8461676836478615	0.15383231635213854	
#> V59 is missing
#> M	R	
#> 0.5326732044900366	0.46732679550996337	
#> 
#> 
#> Weight: 0.64
#> 
#> Decision Stump
#> 
#> Classifications
#> 
#> V28 <= 0.95425 : M
#> V28 > 0.95425 : M
#> V28 is missing : M
#> 
#> Class distributions
#> 
#> V28 <= 0.95425
#> M	R	
#> 0.5390572957843922	0.4609427042156078	
#> V28 > 0.95425
#> M	R	
#> 0.9585642717334097	0.04143572826659032	
#> V28 is missing
#> M	R	
#> 0.6146478470923142	0.3853521529076858	
#> 
#> 
#> Weight: 0.47
#> 
#> Number of performed Iterations: 10
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2028986