Classification Random Forest Learner from Weka

Class for constructing a random forest. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

• 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

• store_out_of_bag_predictions:

  • original id: store-out-of-bag-predictions

• output_out_of_bag_complexity_statistics:

  • original id: output-out-of-bag-complexity-statistics

• num_slots:

  • original id: num-slots

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

• attribute-importance removed:

  • Compute and output attribute importance (mean impurity decrease method)

• Reason for change: The parameter is removed because it's unclear how to actually use it.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.random_forest_weka")
lrn("classif.random_forest_weka")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
P	numeric	100		\([0, 100]\)
O	logical	FALSE	TRUE, FALSE	-
store_out_of_bag_predictions	logical	FALSE	TRUE, FALSE	-
output_out_of_bag_complexity_statistics	logical	FALSE	TRUE, FALSE	-
print	logical	FALSE	TRUE, FALSE	-
I	integer	100		\([1, \infty)\)
num_slots	integer	1		\((-\infty, \infty)\)
K	integer	0		\((-\infty, \infty)\)
M	integer	1		\([1, \infty)\)
V	numeric	0.001		\((-\infty, \infty)\)
S	integer	1		\((-\infty, \infty)\)
depth	integer	0		\([0, \infty)\)
N	integer	0		\((-\infty, \infty)\)
U	logical	FALSE	TRUE, FALSE	-
B	logical	FALSE	TRUE, FALSE	-
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		-

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5--32. ISSN 1573-0565, doi:10.1023/A:1010933404324 .

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

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRandomForestWeka

Methods

Public methods

• LearnerClassifRandomForestWeka$new()

• LearnerClassifRandomForestWeka$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomForestWeka$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomForestWeka$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("classif.random_forest_weka")
print(learner)
#> <LearnerClassifRandomForestWeka:classif.random_forest_weka>: Random Forest
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, multiclass, twoclass

# available parameters:
learner$param_set$ids()
#>  [1] "subset"                                 
#>  [2] "na.action"                              
#>  [3] "P"                                      
#>  [4] "O"                                      
#>  [5] "store_out_of_bag_predictions"           
#>  [6] "output_out_of_bag_complexity_statistics"
#>  [7] "print"                                  
#>  [8] "I"                                      
#>  [9] "num_slots"                              
#> [10] "K"                                      
#> [11] "M"                                      
#> [12] "V"                                      
#> [13] "S"                                      
#> [14] "depth"                                  
#> [15] "N"                                      
#> [16] "U"                                      
#> [17] "B"                                      
#> [18] "output_debug_info"                      
#> [19] "do_not_check_capabilities"              
#> [20] "num_decimal_places"                     
#> [21] "batch_size"                             
#> [22] "options"