Oblique Random Forest Classifier

Accelerated oblique random classification forest. Calls aorsf::orsf() from aorsf. Note that although the learner has the property "missing" and it can in principle deal with missing values, the behavior has to be configured using the parameter na_action.

Initial parameter values

• n_thread: This parameter is initialized to 1 (default is 0) to avoid conflicts with the mlr3 parallelization.

• pred_simplify has to be TRUE, otherwise response is NA in prediction

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.aorsf")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3extralearners, aorsf

Parameters

Id	Type	Default	Levels	Range
attach_data	logical	TRUE	TRUE, FALSE	-
epsilon	numeric	1e-09		\([0, \infty)\)
importance	character	anova	none, anova, negate, permute	-
importance_max_pvalue	numeric	0.01		\([1e-04, 0.9999]\)
leaf_min_events	integer	1		\([1, \infty)\)
leaf_min_obs	integer	5		\([1, \infty)\)
max_iter	integer	20		\([1, \infty)\)
method	character	glm	glm, net, pca, random	-
mtry	integer	NULL		\([1, \infty)\)
mtry_ratio	numeric	-		\([0, 1]\)
n_retry	integer	3		\([0, \infty)\)
n_split	integer	5		\([1, \infty)\)
n_thread	integer	-		\([0, \infty)\)
n_tree	integer	500		\([1, \infty)\)
na_action	character	fail	fail, impute_meanmode	-
net_mix	numeric	0.5		\((-\infty, \infty)\)
oobag	logical	FALSE	TRUE, FALSE	-
oobag_eval_every	integer	NULL		\([1, \infty)\)
oobag_fun	untyped	NULL		-
oobag_pred_type	character	prob	none, leaf, prob, class	-
pred_aggregate	logical	TRUE	TRUE, FALSE	-
sample_fraction	numeric	0.632		\([0, 1]\)
sample_with_replacement	logical	TRUE	TRUE, FALSE	-
scale_x	logical	FALSE	TRUE, FALSE	-
split_min_events	integer	5		\([1, \infty)\)
split_min_obs	integer	10		\([1, \infty)\)
split_min_stat	numeric	NULL		\([0, \infty)\)
split_rule	character	gini	gini, cstat	-
target_df	integer	NULL		\([1, \infty)\)
tree_seeds	integer	NULL		\([1, \infty)\)
verbose_progress	logical	FALSE	TRUE, FALSE	-

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 -> LearnerClassifObliqueRandomForest

Methods

Public methods

• LearnerClassifObliqueRandomForest$new()

• LearnerClassifObliqueRandomForest$oob_error()

• LearnerClassifObliqueRandomForest$importance()

• LearnerClassifObliqueRandomForest$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()

---

LearnerClassifObliqueRandomForest$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifObliqueRandomForest$new()

---

LearnerClassifObliqueRandomForest$oob_error()

OOB concordance error extracted from the model slot eval_oobag$stat_values

Usage

LearnerClassifObliqueRandomForest$oob_error()

Returns

numeric().

---

LearnerClassifObliqueRandomForest$importance()

The importance scores are extracted from the model.

Usage

LearnerClassifObliqueRandomForest$importance()

Returns

Named numeric().

---

LearnerClassifObliqueRandomForest$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifObliqueRandomForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.aorsf")
print(learner)
#> 
#> ── <LearnerClassifObliqueRandomForest> (classif.aorsf): Oblique Random Forest Cl
#> • Model: -
#> • Parameters: n_thread=1
#> • Packages: mlr3, mlr3extralearners, and aorsf
#> • Predict Types: [response] and prob
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, missings, multiclass, oob_error, twoclass, and
#> weights
#> • Other settings: use_weights = 'use', 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$model)
#> ---------- Oblique random classification forest
#> 
#>      Linear combinations: Logistic regression
#>           N observations: 139
#>                N classes: 2
#>                  N trees: 500
#>       N predictors total: 60
#>    N predictors per node: 8
#>  Average leaves per tree: 4.91
#> Min observations in leaf: 5
#>           OOB stat value: 0.87
#>            OOB stat type: AUC-ROC
#>      Variable importance: anova
#> 
#> -----------------------------------------
print(learner$importance())
#>        V11        V20        V12        V36         V9        V10        V13 
#> 0.40807175 0.38604651 0.37327189 0.28251121 0.27272727 0.25454545 0.24789916 
#>        V21        V45        V37        V44        V49        V19        V52 
#> 0.24166667 0.22374429 0.22277228 0.21276596 0.20270270 0.18867925 0.18644068 
#>        V43        V47        V28        V46        V48        V17        V54 
#> 0.18025751 0.17647059 0.17647059 0.17050691 0.16964286 0.15566038 0.14572864 
#>        V51        V22        V16        V31        V27         V1        V35 
#> 0.14479638 0.14102564 0.14090909 0.14084507 0.12437811 0.12385321 0.12107623 
#>        V18        V34        V29        V40        V38        V14        V42 
#> 0.09569378 0.08780488 0.08653846 0.08260870 0.07906977 0.07246377 0.07109005 
#>         V2         V8        V23        V30        V41         V4        V39 
#> 0.06837607 0.06730769 0.06696429 0.06666667 0.06392694 0.06225681 0.05687204 
#>        V26        V15         V6        V58        V33         V5        V32 
#> 0.05485232 0.05485232 0.05288462 0.04932735 0.04878049 0.04661017 0.04366812 
#>        V53         V3        V59        V57        V50        V56        V55 
#> 0.04310345 0.04219409 0.03921569 0.03286385 0.03255814 0.02692308 0.02643172 
#>        V25        V60        V24         V7 
#> 0.02542373 0.01941748 0.01492537 0.01401869 

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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.1884058