# \[ANN\] BetaML.jl.. yet an other (simple) Machine Learning Package

**URL:** <https://discourse.julialang.org/t/ann-betaml-jl-yet-an-other-simple-machine-learning-package/41057>\
**Category:** Package Announcements\
**Tags:** package, announcement, machine-learning\
**Created:** [June 9, 2020, 10:14am UTC](https://discourse.julialang.org/t/ann-betaml-jl-yet-an-other-simple-machine-learning-package/41057 "2020-06-09T10:14:25Z")\
**Posts on this page:** 1\
**Showing post:** 17

<div class="post-metadata">

**Author:** ![sylvaticus](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sylvaticus/32/203883_2.png) [@sylvaticus](https://discourse.julialang.org/u/sylvaticus)\
**Post date:** [May 15, 2024, 8:47pm UTC](https://discourse.julialang.org/t/ann-betaml-jl-yet-an-other-simple-machine-learning-package/41057/17 "2024-05-15T20:47:37Z")

</div>

[BetaML](https://github.com/sylvaticus/BetaML.jl) v0.12 is out

Release notes:

- Added `FeatureRanker`, a flexible feature ranking estimator using multiple feature importance metrics
- new functions `kl_divergence` and `sobol_index`
- added option to RF/DT models to ignore specific variables in prediction, by following _both_ the splits on nodes occurring on that dimensions, as the keyword `ignore_dims` to the `predict` function
- added option `sampling_share` to `RandomForestEstimator` model
- DOC: added Benchmarks (but then temporarily removed due to the issue of SystemBenchmark not installable, see [this issue](https://github.com/IanButterworth/SystemBenchmark.jl/issues/64) )
- DOC: added `FeatureRanker` tutorial
- bugfix on `l2loss_by_cv` for unsupervised models

A specific announcement for [`FeatureRanker`](https://sylvaticus.github.io/BetaML.jl/dev/Utils.html#BetaML.Utils.FeatureRanker) is posted [here](https://discourse.julialang.org/t/ann-featureranking-learn-which-variables-contribute-the-most-to-the-estimation-of-black-box-models/114321).

---

_[View the full topic](https://discourse.julialang.org/t/ann-betaml-jl-yet-an-other-simple-machine-learning-package/41057)._