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Probabilistic Supervised Learning for mlr3.

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What is mlr3proba ?

mlr3proba is a machine learning toolkit for making probabilistic predictions within the mlr3 ecosystem. It currently supports the following tasks:

Key features of mlr3proba are

mlr3proba makes use of the distr6 probability distribution interface as its probabilistic predictive return type.

Feature Overview

The current mlr3proba release focuses on survival analysis, and contains:


The vision of mlr3proba is to provide comprehensive machine learning functionality to the mlr3 ecosystem for continuous probabilistic return types.

The lifecycle of the survival task and features are considered maturing and any major changes are unlikely.

The density and probabilistic supervised regression tasks are currently in the early stages of development. Task frameworks have been drawn up, but may not be stable; learners need to be interfaced, and contributions are very welcome (see issues).


Install the last release from CRAN:


Install the development version from GitHub:


Survival Analysis

Survival Learners

ID Learner Package
surv.blackboost Gradient Boosting with Regression Trees mboost
surv.coxph Cox Proportional Hazards survival
surv.cvglmnet Cross-Validated GLM with Elastic Net Regularization glmnet
surv.flexible Flexible Parametric Spline Models flexsurv
surv.gamboost Gradient Boosting for Additive Models mboost
surv.gbm Generalized Boosting Regression Modeling gbm
surv.glmboost Gradient Boosting with Component-wise Linear Models mboost
surv.glmnet GLM with Elastic Net Regularization glmnet
surv.kaplan Kaplan-Meier Estimator survival
surv.mboost Gradient Boosting for Generalized Additive Models mboost
surv.nelson Nelson-Aalen Estimator survival
surv.parametric Fully Parametric Survival Models survival
surv.penalized L1 and L2 Penalized Estimation in GLMs penalized
surv.randomForestSRC RandomForestSRC Survival Forest randomForestSRC
surv.ranger Ranger Survival Forest ranger
surv.rpart Rpart Survival Forest rpart
surv.svm Regression, Ranking and Hybrid Support Vector Machines survivalsvm

Survival Measures

ID Learner Package
surv.beggC Begg’s C-Index survAUC
surv.chamblessAUC Chambless and Diao’s AUC survAUC
surv.gonenC Gonen and Heller’s C-Index survAUC
surv.graf Integrated Graf Score mlr3proba
surv.grafSE Standard Error of Integrated Graf Score mlr3proba
surv.harrellC Harrell’s C-Index mlr3proba
surv.hungAUC Hung and Chiang’s AUC survAUC
surv.intlogloss Integrated Log Loss mlr3proba
surv.intloglossSE Standard Error of Integrated Log Loss mlr3proba
surv.logloss Log Loss mlr3proba
surv.loglossSE Standard Error of Log Loss mlr3proba
surv.nagelkR2 Nagelkerke’s R2 survAUC
surv.oquigleyR2 O’Quigley, Xu, and Stare’s R2 survAUC
surv.songAUC Song and Zhou’s AUC survAUC
surv.songTNR Song and Zhou’s TNR survAUC
surv.songTPR Song and Zhou’s TPR survAUC
surv.unoAUC Uno’s AUC survAUC
surv.unoC Uno’s C-Index survAUC
surv.unoTNR Uno’s TNR survAUC
surv.unoTPR Uno’s TPR survAUC
surv.xuR2 Xu and O’Quigley’s R2 survAUC

Near-Future Plans

Bugs, Questions, Feedback

mlr3proba is a free and open source software project that encourages participation and feedback. If you have any issues, questions, suggestions or feedback, please do not hesitate to open an “issue” about it on the GitHub page!

In case of problems / bugs, it is often helpful if you provide a “minimum working example” that showcases the behaviour (but don’t worry about this if the bug is obvious).

Similar Projects

Predecessors to this package are previous instances of survival modelling in mlr. The skpro package in the python/scikit-learn ecosystem follows a similar interface for probabilistic supervised learning and is an architectural predecessor. Several packages exist which allow probabilistic predictive modelling with a Bayesian model specific general interface, such as jags and stan. For implementation of a few survival models and measures, a central package is survival. There does not appear to be a package that provides an architectural framework for distribution/density estimation, see this list for a review of density estimation packages in R.