Skip to content

@nhejazi nhejazi released this Oct 8, 2019 · 3 commits to master since this release

v1.3.0 of sl3 represents a set of major updates to the core software. An inexhaustive list of the included changes include

  • fixing incorrect handling of missingness in the automatic imputation procedure
  • addition of new standard learners, including from the gam and caret packages
  • addition of custom learners for conditional density estimation, including semiparametric methods based on conditional mean and conditional mean/variance estimation as well as generalized functionality for density estimation via a pooled hazards approach
Assets 2

@nhejazi nhejazi released this Jun 21, 2019 · 21 commits to master since this release

v1.2.0 of sl3 represents a set of major updates to the core software. An inexhaustive list of the included changes include

  • default metalearners based on task outcome types
  • handling of imputation internally in task objects
  • addition of new learners, including from the gbm, earth, polspline packages
  • fixing errors in existing learners (e.g., subtle parallelization in xgboost and ranger)
  • support for multivariate outcomes and (default) revere-style cross-validation
  • support for cross-validated super learner and variable importance
Assets 2

@nhejazi nhejazi released this Aug 9, 2018 · 196 commits to master since this release

v1.1.0 of the sl3 R package marks a full-featured and stable release of the project. Numerous learners are included and many bugs have been fixed relative to earlier versions (esp v1.0.0) of the software.

Assets 2
You can’t perform that action at this time.