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$model)
#> ---------- Oblique random classification forest
#> 
#>      Linear combinations: Logistic regression
#>           N observations: 139
#>                N classes: 2
#>                  N trees: 500
#>       N predictors total: 60
#>    N predictors per node: 8
#>  Average leaves per tree: 5.178
#> Min observations in leaf: 5
#>           OOB stat value: 0.89
#>            OOB stat type: AUC-ROC
#>      Variable importance: anova
#> 
#> -----------------------------------------
print(learner$importance())
#>        V12        V11        V47        V36        V45        V28        V46 
#> 0.35443038 0.35000000 0.34403670 0.33333333 0.30530973 0.26639344 0.25311203 
#>        V49        V10        V13         V9        V44        V48        V51 
#> 0.24701195 0.24324324 0.22666667 0.22395833 0.21719457 0.21621622 0.20673077 
#>        V37         V4        V20        V21        V29        V43         V1 
#> 0.20627803 0.17621145 0.16972477 0.16666667 0.14285714 0.14220183 0.12556054 
#>        V35        V41        V52        V22        V19        V40        V57 
#> 0.12217195 0.12037037 0.11255411 0.11206897 0.10679612 0.10550459 0.10000000 
#>        V16        V42        V39        V58        V31         V2        V27 
#> 0.09745763 0.09615385 0.09417040 0.09132420 0.08835341 0.08658009 0.08571429 
#>        V18         V5        V17        V34        V53        V59        V23 
#> 0.08520179 0.08490566 0.08000000 0.07983193 0.07575758 0.07500000 0.06930693 
#>        V15        V30        V55        V54        V38         V7         V8 
#> 0.06532663 0.06074766 0.05809129 0.05339806 0.05263158 0.04663212 0.04347826 
#>        V14         V3        V33        V26        V50        V24        V25 
#> 0.04306220 0.04245283 0.04090909 0.03738318 0.03539823 0.03375527 0.03097345 
#>        V60        V56        V32         V6 
#> 0.02928870 0.02469136 0.02000000 0.01694915 

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

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