R/LearnerSurvBlackboost.R
LearnerSurvBlackboost.RdCalls mboost::blackboost().
lp is predicted by mboost::predict.mboost()
distr is predicted by mboost::survFit() which assumes a PH fit with a Breslow estimator
crank is identical to lp
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.
R6::R6Class() inheriting from LearnerSurv.
LearnerSurvBlackboost$new()
mlr_learners$get("surv.blackboost")
lrn("surv.blackboost")
Type: "surv"
Predict Types: distr, crank, lp
Feature Types: integer, numeric, factor
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.
Other survival learners:
LearnerSurvCVGlmnet,
LearnerSurvCoxPH,
LearnerSurvFlexible,
LearnerSurvGBM,
LearnerSurvGamboost,
LearnerSurvGlmboost,
LearnerSurvGlmnet,
LearnerSurvKaplan,
LearnerSurvMboost,
LearnerSurvNelson,
LearnerSurvParametric,
LearnerSurvPenalized,
LearnerSurvRandomForestSRC,
LearnerSurvRanger,
LearnerSurvRpart,
LearnerSurvSVM
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