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 "custom.family" then an object of class mboost::Family() needs to be passed to the custom.family parameter.

Format

R6::R6Class() inheriting from LearnerSurv.

Construction

LearnerSurvBlackboost$new()
mlr_learners$get("surv.blackboost")
lrn("surv.blackboost")

Meta Information

References

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. http://www.jstor.org/stable/2699986.

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

See also

Examples

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