Calls mboost::blackboost().

The dist parameter is specified slightly differently than in mboost. Whereas the latter takes in objects, in this learner instead a string is specified in order to identify which distribution to use. As the default in mboost is the Gaussian family, which is not compatible with survival models, instead we have by default "coxph".

If the value given to the Family parameter is "" then an object of class mboost::Family() needs to be passed to the parameter.


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


Meta Information


Bühlmann P, Hothorn T (2007). “Boosting Algorithms: Regularization, Prediction and Model Fitting.” Statistical Science, 22(4), 477--505. doi: 10.1214/07-sts242 .

Hothorn T, Hornik K, Zeileis A (2006). “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics, 15(3), 651--674. doi: 10.1198/106186006x133933 .

Freund Y, Schapire RE, others (1996). “Experiments with a new boosting algorithm.” In In: Thirteenth International Conference on ML, volume 96, 148--156. Citeseer.

Friedman JH (2001). “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics, 29(5), 1189--1232.

Ridgeway G (1999). “The state of boosting.” Computing Science and Statistics, 172--181.

See also

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvBlackboost


Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.



Method clone()

The objects of this class are cloneable with this method.


LearnerSurvBlackboost$clone(deep = FALSE)



Whether to make a deep clone.


library(mlr3) task = tgen("simsurv")$generate(20) learner = lrn("surv.blackboost") resampling = rsmp("cv", folds = 2) resample(task, learner, resampling)
#> <ResampleResult> of 2 iterations #> * Task: simsurv #> * Learner: surv.blackboost #> * Warnings: 0 in 0 iterations #> * Errors: 0 in 0 iterations