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Explainable machine learning with mlr3 and DALEX

Explainable machine learning with mlr3 and DALEX

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...

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mlr3 tutorial on useR!2020muc

mlr3 tutorial at the useR!2020 European hub About the useR!2020 satellite event in Munich mlr3 tutorial at the useR!2020 European hub We are thrilled that we got accepted for a tutorial at the useR!2020 satellite event in Munich on July 7th. B...

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mlr wins Open Source Machine Learning Software Award

mlr wins Open Source Machine Learning Software Award

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...

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Introducing mlrPlayground

Introducing mlrPlayground

First of all The idea The features Usage First of all You may ask yourself how is this name ‘mlrPlayground’ even justified? What a person dares to put two such opposite terms in a single word and expects people to take him seriously? I as...

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mlr-2.15.0

Changes to benchmark() Changes to Filters New ensemble filters New return structure for filter values Learners References We just released mlr v2.15.0 to CRAN. This version includes some breaking changes and the usual bug fixes from the last three months. We made good progress on the goal of cleaning up the Github repo. We processed nearly all open pull requests (around 40). In the next months we will focus...

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mlr3-0.1.0

mlr3-0.1.0

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...

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mlr + drake: Reproducible machine-learning workflow management

mlr + drake: Reproducible machine-learning workflow management

You may have heard about the drake package. It got a lot attention recently in the R community because it simplifies reproducible workflow management. This comes especially handy for large projects which have hundreds of intermediate steps. Built-in High-Performance-Cluster (HPC) support and graph visualization are just two goodies that come on top of the basic functionality. drake is able to track changes in...

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mlr-2.14.0

Filters Learners Resampling mlr-org NEWS Roadmap for mlr 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 highligh...

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Visualization of spatial cross-validation partitioning

Visualization of spatial cross-validation partitioning

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 Brenning (2005) is used to partition the dataset into equally sized, spatially disjoint subsets. See also this...

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Training Courses for mlr: Machine Learning in R

Training Courses for mlr: Machine Learning in R

The mlr: Machine Learning in R package provides a generic, object-oriented and extensible framework for classification, regression, survival analysis and clustering for the statistical programming language R. The package targets practitioners who want ...

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