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$model)
#> ---------- Oblique random classification forest
#> 
#>      Linear combinations: Logistic regression
#>           N observations: 139
#>                N classes: 2
#>                  N trees: 500
#>       N predictors total: 60
#>    N predictors per node: 8
#>  Average leaves per tree: 4.918
#> Min observations in leaf: 5
#>           OOB stat value: 0.91
#>            OOB stat type: AUC-ROC
#>      Variable importance: anova
#> 
#> -----------------------------------------
print(learner$importance())
#>         V11         V12         V13         V36         V37         V44 
#> 0.523560209 0.501915709 0.355000000 0.354679803 0.315789474 0.291666667 
#>          V9          V4         V49         V10         V48         V45 
#> 0.240174672 0.235294118 0.231884058 0.230392157 0.217777778 0.215962441 
#>         V43          V1         V35         V52         V22         V21 
#> 0.191588785 0.183673469 0.181415929 0.165254237 0.159183673 0.142857143 
#>         V47         V16         V23         V20         V17         V59 
#> 0.136792453 0.133620690 0.131004367 0.126903553 0.120689655 0.115384615 
#>         V34         V31         V46         V54         V28         V38 
#> 0.113122172 0.103004292 0.102459016 0.100478469 0.100456621 0.098039216 
#>         V58         V30          V2         V15         V19         V39 
#> 0.097560976 0.092233010 0.090000000 0.086124402 0.079439252 0.075376884 
#>         V42         V29         V18         V24         V14         V26 
#> 0.072727273 0.072340426 0.066985646 0.066350711 0.061674009 0.059113300 
#>          V8         V51         V27         V40         V55         V25 
#> 0.058035714 0.052631579 0.050251256 0.047393365 0.044554455 0.043478261 
#>         V56         V33          V7         V41          V6         V60 
#> 0.036697248 0.033195021 0.033175355 0.030434783 0.028301887 0.026548673 
#>          V5         V32         V50         V53         V57          V3 
#> 0.025000000 0.023148148 0.022935780 0.020000000 0.014084507 0.008849558 

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

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