Publications by r-bloggers on Machine Learning in R
Parameter tuning with mlrHyperopt
Hyperparameter tuning with mlr is rich in options as they are multiple tuning methods: Simple Random Search Grid Search Iterated F-Racing (via irace) Sequential Model-Based Optimization (via mlrMBO) Also the search space is easily definable and customizable for each of the 60+ learners of mlr using the ParamSets from the ParamHelpers Package. T...
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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 follo...
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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 to quickly apply machine learning algorithms, as well as researchers who want to implement, benc...
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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 analysi...
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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 appro...
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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 highlight some of the new implementations that come along with this release of v2.14.0 Filters We integra...
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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 t...
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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 with a clean object-oriented-design using the R6 class system. With this, it overcomes the limitati...
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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 ...
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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 assume most of you know ‘mlr’, for those who don’t: It is a framework offering a huge variety of tool...
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