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The last mlr release was in August 2018 – so it was definitely time for a new release after around 9 months of development!

The NEWS file can be found directly here.

In this post we highlight some of the new implementations that come along with this release of v2.14.0

## Filters

We integrated the filter methods from the praznik package. These were quite few:

• praznik_JMI
• praznik_DISR
• praznik_JMIM
• praznik_MIM
• praznik_NJMIM
• praznik_MRMR
• praznik_CMIM

Also, a long awaited PR that we finally merged was the inclusion of the FSelectorRcpp filters. These are around 100 times faster than the Java-driven ones from the FSelector package.

In addition, we are now using a consistent naming scheme for the filters following _. This change might break your existing code if you used mlr filters before. However, since it is just a naming change we think the burden of updating your code is acceptable.

## Learners

• classif.liquidSVM
• regr.liquidSVM

Learner regr.h2o.gbm now uses “h2o.use.data.table” = TRUE` by default which should result in a runtime increase.

Also, it is now possible to retrieve the feature importance of h2O learners.

## Resampling

You can now provide fully predefined indices in resampling. This is useful for datasets that have a certain grouping structure (e.g. spatial data) to maintain this grouping.

## mlr-org NEWS

You might be wondering what we’ve been up to in the last months in our group. The major project that we started was mlr3. This is a clean rewrite of mlr with the concept of a modular structure aiming to simplify usage and maintenance of the “mlr idea” in the future, both for users and developers. We are not completely finished yet but you can take a look at the Github repo what we have achieved so far. Once we are ready to release the initial version, we will of course write a dedicated post about it.

Putting a lot of time into mlr3 also lead to the fact of having less time for responding to issues and questions in mlr. We would like to apologize for this. All of us are working in academia and are not getting paid for mlr. So our resources are limited to some extent. We very welcome anyone that would like to help us and get involved into mlr or mlr3. Our team is not a closed group and anyone can contribute to the mlr-org projects.

This fact also lead to the most recent maintainer change of mlr. As Bernd Bischl (the creator and maintainer) of mlr has a lot of duties, we decided to make Lars Kotthoff and Patrick Schratz the new maintainers of the mlr package.

Due to the existence of mlr3, most development will go there and mlr is aimed to receive only bug fixes and some clean up in the near future. Right now, we have over 400 issues and 30 pull requests so there is a still a lot to do 🙂