NEWS
caretEnsemble 4.0.2
New Features
- Add option to keep resamples for each repeated fold in
caretStack and caretList, rather than
aggregating to one resample per row in the original data. This can give
your stacking model more variance to work with, but can lead to a lot
more issues with aligning predictions from different models,
particularly ones that use different resampling strategies.
- Add an option to include original features from the raw data in the
stack, if stacking on a new dataset rather than on stacked
predictions.
- Add
plot_variable_importance() to visualize variable
importance for caretStack and caretEnsemble
models.
caretEnsemble 4.0.1
Improvements
- Added
aggregate_resamples option to
caretStack and related functions to control whether
resamples are aggregated.
- Speed up the example for
autoplot so it runs in <1
second on most platforms.
caretEnsemble 4.0.0
Major Changes
- Multiclass support!
caretList, caretStack,
and caretEnsemble.
- The greedy optimizer is back!
caretEnsemble now uses a
greedy optimizer by default. This optimizer can never be worse than the
worst single model. caretStack still supports all caret
models, including glm.
Internal Changes
- Refactored some internals for scalability
(e.g.
data.table for predictions, trim some un-needed data
by default).
- Moved all the S3 methods to
caretStack, which now
supports print, summary, plot,
dotplot, and autoplot.
caretEnsemble inherits from caretStack, and
therefore also supports all of these methods.
- Allow ensembling of mixed lists of classification and regression
models.
- Allow ensemble of models with different resampling strategies, so
long as they were trained on the same data.
- Allow transfer learning for ensembling models trained on different
datasets.
- Added permutation importance as the default importance method for
caretLists and caretStacks.
- Add a default
trainControl constructor to make it
easier to build good controls for training caretLists for
stacking with caretStack.
- Expanded test coverage to 100%.
- Sped up test suite (unit tests now run in 20 seconds).
- Delinted codebase: now conforms with all available linters save the
object name linter.
- Added a makefile for easier local package development.
- Fixed badges in the readme.
- Added a pkgdown site.
- Switched to GitHub Actions (from Travis) for CI.
- Internal refactoring, optimization, and bug fixes.
caretEnsemble 2.0.3
Bug Fixes
- Fix broken package documentation with new roxygen2.
- Replace deprecated linters with the new versions.
caretEnsemble 2.0.2
Bug Fixes
- Fix broken tests on r-devel.
caretEnsemble 2.0.1
Minor Fixes
- Minor fixes to support R 4.0.
caretEnsemble 2.0.0
Major Changes
caretEnsemble now inherits from
caretStack.
- Removed the optimizers and now use a
glm for
caretEnsemble (optimizers will be added back as
caret.train models in a future release).
- Cleaned up namespace (all dependencies are explicit imports, rather
than implicit imports or dependencies).
- Removed S3 functions that are not really S3 functions
(e.g.
autoplot and fortify). We will either
make those true S3 classes, or inherit from the packages that define
them in a future release.
- Fixed the build on Travis and locally.
caretEnsemble 1.0.5
Improvements
- Change output for predict functions to better align with other
predict methods in R (
predict.caretEnsemble and
predict.caretStack).
- Update documentation for predict methods to better explain the model
disagreement calculation.
- Speed and memory improvements by switching to
data.table for some internals.
- Modified the formula for a weighted standard deviation in the model
disagreement calculation.
caretEnsemble 1.0 -
First CRAN release
Introduction
caretEnsemble is a new package for making ensembles of
caret
models.