Articles by r-bloggers on Machine Learning in R


August 5, 2019 | r-bloggers on Machine Learning in R

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 ... [Read more...]


July 30, 2019 | r-bloggers on Machine Learning in R

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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April 17, 2019 | r-bloggers on Machine Learning in R

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... [Read more...]

Parameter tuning with mlrHyperopt

July 18, 2017 | r-bloggers on Machine Learning in R

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 ...
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