Accelerated Oblique Random Survival Forest Learner
mlr_learners_surv.aorsf.Rd
Accelerated oblique random survival 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 behaviour has to be configured using the parameter na_action
.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
Meta Information
Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3proba, mlr3extralearners, aorsf, pracma
Parameters
Id | Type | Default | Levels | Range |
n_tree | integer | 500 | \([1, \infty)\) | |
n_split | integer | 5 | \([1, \infty)\) | |
n_retry | integer | 3 | \([0, \infty)\) | |
n_thread | integer | 0 | \([0, \infty)\) | |
pred_aggregate | logical | TRUE | TRUE, FALSE | - |
pred_simplify | logical | FALSE | TRUE, FALSE | - |
mtry | integer | NULL | \([1, \infty)\) | |
mtry_ratio | numeric | - | \([0, 1]\) | |
sample_with_replacement | logical | TRUE | TRUE, FALSE | - |
sample_fraction | numeric | 0.632 | \([0, 1]\) | |
control_type | character | fast | fast, cph, net | - |
split_rule | character | logrank | logrank, cstat | - |
control_fast_do_scale | logical | FALSE | TRUE, FALSE | - |
control_fast_ties | character | efron | efron, breslow | - |
control_cph_ties | character | efron | efron, breslow | - |
control_cph_eps | numeric | 1e-09 | \([0, \infty)\) | |
control_cph_iter_max | integer | 20 | \([1, \infty)\) | |
control_net_alpha | numeric | 0.5 | \((-\infty, \infty)\) | |
control_net_df_target | integer | NULL | \([1, \infty)\) | |
leaf_min_events | integer | 1 | \([1, \infty)\) | |
leaf_min_obs | integer | 5 | \([1, \infty)\) | |
split_min_events | integer | 5 | \([1, \infty)\) | |
split_min_obs | integer | 10 | \([1, \infty)\) | |
split_min_stat | numeric | NULL | \([0, \infty)\) | |
oobag_pred_type | character | surv | none, surv, risk, chf | - |
importance | character | anova | none, anova, negate, permute | - |
importance_max_pvalue | numeric | 0.01 | \([1e-04, 0.9999]\) | |
oobag_pred_horizon | numeric | NULL | \([0, \infty)\) | |
oobag_eval_every | integer | NULL | \([1, \infty)\) | |
attach_data | logical | TRUE | TRUE, FALSE | - |
verbose_progress | logical | FALSE | TRUE, FALSE | - |
na_action | character | fail | fail, omit, impute_meanmode | - |
Initial parameter values
mtry
:This hyperparameter can alternatively be set via the added hyperparameter
mtry_ratio
asmtry = max(ceiling(mtry_ratio * n_features), 1)
. Note thatmtry
andmtry_ratio
are mutually exclusive.
References
Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min Y, Mcclure LA, Howard G, Simon N (2019). “Oblique random survival forests.” The Annals of Applied Statistics, 13(3). doi:10.1214/19-aoas1261 .
Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM (2023). “Accelerated and interpretable oblique random survival forests.” Journal of Computational and Graphical Statistics, 1--16. doi:10.1080/10618600.2023.2231048 .
See also
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.
Super classes
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvAorsf
Methods
Method oob_error()
OOB concordance error extracted from the model slot
eval_oobag$stat_values
Examples
learner = mlr3::lrn("surv.aorsf")
print(learner)
#> <LearnerSurvAorsf:surv.aorsf>: Oblique Random Forest
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, aorsf, pracma
#> * Predict Types: [crank], distr
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: importance, missings, oob_error
# available parameters:
learner$param_set$ids()
#> [1] "n_tree" "n_split"
#> [3] "n_retry" "n_thread"
#> [5] "pred_aggregate" "pred_simplify"
#> [7] "mtry" "mtry_ratio"
#> [9] "sample_with_replacement" "sample_fraction"
#> [11] "control_type" "split_rule"
#> [13] "control_fast_do_scale" "control_fast_ties"
#> [15] "control_cph_ties" "control_cph_eps"
#> [17] "control_cph_iter_max" "control_net_alpha"
#> [19] "control_net_df_target" "leaf_min_events"
#> [21] "leaf_min_obs" "split_min_events"
#> [23] "split_min_obs" "split_min_stat"
#> [25] "oobag_pred_type" "importance"
#> [27] "importance_max_pvalue" "oobag_pred_horizon"
#> [29] "oobag_eval_every" "attach_data"
#> [31] "verbose_progress" "na_action"