# \[ANN\] BetaML v 0.7 New Missing values imputers and "standardised" fit!/predict API

**URL:** <https://discourse.julialang.org/t/ann-betaml-v-0-7-new-missing-values-imputers-and-standardised-fit-predict-api/85167>\
**Category:** Package Announcements\
**Created:** [August 2, 2022, 2:15pm UTC](https://discourse.julialang.org/t/ann-betaml-v-0-7-new-missing-values-imputers-and-standardised-fit-predict-api/85167 "2022-08-02T14:15:06Z")\
**Posts on this page:** 1\
**Page:** 1

<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:** [August 2, 2022, 2:15pm UTC](https://discourse.julialang.org/t/ann-betaml-v-0-7-new-missing-values-imputers-and-standardised-fit-predict-api/85167/1 "2022-08-02T14:15:06Z")

</div>

Hello all, I have just released a “big” new release of [BetaML.jl](https://github.com/sylvaticus/BetaML.jl), the Machine Learning toolbox of the Bureau d’Économie Théorique et Appliquée (“BETA”) of Nancy, a repository with several Machine Learning algorithms (linear classifiers, random forests, neural networks, k-means/medoids, Gaussian Mixture Models…) and utility functions (samples, partitioners, encoders, fitting measures, scalers, cross-validation, confusion matrix…) in pure Julia.

These are the release notes:

- new experimental “V2” API that implements a “standard” `mod = Model([Options])`, `train!(mod,X,[Y])`, `predict(mod,[X])` workflow. In BetaML v0.7 this new API is still experimental, as documentation and implementation are not completed (missing yet perceptrons and neural networks). We plan to make it the default API in BetaML 0.8, when the current API will be dimmed deprecated.
- new `Imputation` module with several missing values imputers `MeanImputer`, `GMMImputer`, `RFImputer`, `GeneralImputer` and relative MLJ interfaces. The last one, in particular, allows using any regressor/classifier (not necessarily of BetaML) for which the API described above is valid
- `Cluster` module reorganised with only hard clustering algorithms (K-Means and K-medoids), while GMM clustering and the new `GMMRegressor1` and `GMMRegressor2` (different strategies over the underlying EM algorithm) are in the new `GMM` module
- Split large files in subfiles, like `Trees.jl` where DT and RF are now on separate (included) files
- New `oneHotDecoder(x)` function in `Utils` module
- New dependency to `DocStringExtensions.jl`
- Several bugfixes