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NEWS.md

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sl3 1.4.3

  • Additional arguments for 'Keras' learners Lrnr_lstm_keras and Lrnr_gru_keras provide support for callback functions list and 2-layer networks. Default callbacks list provides early stopping criteria with respect to 'Keras' defaults and patience of 10 epochs. Also, these two 'Keras' learners now call args_to_list upon initialization, and set verbose argument according to options("keras.fit_verbose") or options("sl3.verbose").
  • Update Lrnr_xgboost to support prediction tasks consisting of one observation (e.g., leave-one-out cross-validation).
  • Update Lrnr_sl by adding a new private slot .cv_risk to store the risk estimates, using this to avoid unnecessary re-computation in the print method (the .cv_risk slot is populated on the first print call, and only ever re-printed thereafter).
  • Update documentation of default_metalearner to use native markdown tables.
  • Fix Lrnr_screener_importance's pairing of (a) covariates returned by the importance function with (b) covariates as they are defined in the task. This issue only arose when discrete covariates were automatically one-hot encoded upon task initiation (i.e., when colnames(task$X) != task$nodes$covariates).
  • Reformat importance_plot to plot variables in decreasing order of importance, so most important variables are placed at the top of the dotchart.
  • Enhanced functionality in sl3 task's add_interactions method to support interactions that involve factors. This method is most commonly used by Lrnr_define_interactions, which is intended for use with another learner (e.g., Lrnr_glmnet or Lrnr_glm) in a Pipeline.
  • Modified Lrnr_gam formula (if not specified by user) to not use mgcv's default k=10 degrees of freedom for each smooth s term when there are less than k=10 degrees of freedom. This bypasses an mgcv::gam error, and tends to be relevant only for small n.
  • Added options(java.parameters = "-Xmx2500m") and warning message when Lrnr_bartMachine is initialized, if this option has not already been set. This option was incorporated since the default RAM of 500MB for a Java virtual machine often errors due to memory issues with Lrnr_bartMachine.

sl3 1.4.2

  • Updates to variable importance functionality, including calculation of risk ratio and risk differences under covariate deletion or permutation.
  • Addition of a importance_plot to summarize variable importance findings.
  • Additions of new methods reparameterize and retrain to Lrnr_base, which allows modification of the covariate set while training on a conserved task and prediction on a new task using previously trained learners, respectively.

sl3 1.4.1

  • [TODO]

sl3 1.4.0

  • [TODO]

sl3 1.3.9

  • [TODO]

sl3 1.3.8

  • Updates to variable importance functionality, including use of risk ratios.
  • Change Lrnr_hal9001 and Lrnr_glmnet to respect observation-level IDs.
  • Removal of Remotes and deprecation of Lrnr_rfcde and Lrnr_condensier:
    • Both of these learner classes provided support for conditional density estimation (CDE) and were useful when support for CDE was more limited. Unfortunately, both packages are un-maintained or updated only very sporadically, resulting in both frequent bugs and presenting an obstacle for an eventual CRAN release (both packages are GitHub-only).
    • Lrnr_rfcde wrapped https://github.com/tpospisi/RFCDE, a sporadically maintained tool for conditional density estimation (CDE). Support for this has been removed in favor of built-in CDE tools, including, among others, Lrnr_density_semiparametric.
    • Lrnr_condensier wrapped https://github.com/osofr/condensier, which provided a pooled hazards approach to CDE. This package contained an implementation error (osofr/condensier#15) and was removed from CRAN. Support for this has been removed in favor of Lrnr_density_semiparametric and Lrnr_haldensify, both of which more reliably provide CDE support.

sl3 1.3.7

  • Sampling methods for Monte Carlo integration and related procedures.
  • A metalearner for the cross-validation selector (discrete super learner).
  • A learner for bounding, including support for bounded losses.
  • Resolution of a number of older issues (see #264).
  • Relaxation of checks inside Stack objects for time series learners.
  • Addition of a learner property table to README.Rmd.
  • Maintenance and documentation updates.

sl3 1.3.5

  • Overhaul of data preprocessing.
  • New screening methods and convex combination in Lrnr_nnls.
  • Bug fixes, including covariate subsetting and better handling of NAs.
  • Package and documentation cleanup; continuous integration and testing fixes.
  • Reproducibility updates (including new versioning and DOI minting).

sl3 1.3.0

  • Fixes incorrect handling of missingness in the automatic imputation procedure.
  • Adds new standard learners, including from the gam and caret packages.
  • Adds 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.

sl3 1.2.0

  • Default metalearners based on task outcome types.
  • Handling of imputation internally in task objects.
  • Addition of several 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
  • Sets default cross-validation to be revere-style.
  • Support for cross-validated super learner and variable importance.

sl3 1.1.0

  • 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.

sl3 1.0.0

  • An initial stable release.