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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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Interpretable Machine Learning with iml and mlr

Interpretable Machine Learning with iml and mlr

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 any black box machine learning model: Feature importance: Which were the most important...

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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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Stepwise Bayesian Optimization with mlrMBO

Stepwise Bayesian Optimization with mlrMBO

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 evaluations. In the following we will demonstrate this feature on a simple example. First we...

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