We are happy to announce that a new extension package has joined the CRAN family of mlr3 packages. mlr3spatiotempcv was in the works for more than a year and adds spatiotemporal resampling methods to the mlr3 ecosystem. Such dedicated resampling ...

Tired of learning to use multiple packages to access clustering algorithms? Using different packages makes it difficult to compare the performance of clusterers? It would be great to have just one package that makes interfacing all things clustering easy? mlr3cluster to the rescue! mlr3cluster is a cluster analysis ...

Przemysław Biecek and Szymon Maksymiuk added a new chapter to the mlr3 book on how to analyze machine learning models fitted with mlr3 using the excellent DALEX package. The contributed chapter covers an analysis of a random regression forest (impl...

Przemysław Biecek and Szymon Maksymiuk added a new chapter to the mlr3 book on how to analyze machine learning models fitted with mlr3 using the excellent DALEX package. The contributed chapter covers an analysis of a random regression forest (impl...

Przemysław Biecek and Szymon Maksymiuk added a new chapter to the mlr3 book on how to analyze machine learning models fitted with mlr3 using the excellent DALEX package. The contributed chapter covers an analysis of a random regression forest (imp...

mlr receives Open Source Machine Learning Project Award mlr receives Open Source Machine Learning Project Award We’re extremely proud to have received the Open Source Machine Learning Project Award at ODSC West 2019. We were joined by Tens...

mlr3 - Initial release Background - why a rewrite? The new mlr3 package framework mlr3 at useR!2019 mlr3 - Initial release The mlr-org team is very proud to present the initial release of the mlr3 machine-learning framework for R. mlr3 comes wi...

Introduction Visualization of partitions Multiple resample objects References Introduction In July mlr got a new feature that extended the support for spatial data: The ability to visualize spatial partitions in cross-validation (CV) 9d4f3. When one uses the resampling descriptions “SpCV” or “SpRepCV” in mlr, the k-means clustering approach after ...

Machine learning models repeatedly outperform interpretable, parametric models like the linear regression model. The gains in performance have a price: The models operate as black boxes which are not interpretable. Fortunately, there are many methods that can make machine learning models interpretable. The R package iml provides tools for analysing ...

With the release of the new version of mlrMBO we added some minor fixes and added a practical feature called Human-in-the-loop MBO. It enables you to sequentially visualize the state of the surrogate model, obtain the suggested parameter configuration for the next iteration and update the surrogate model with arbitrary ...