Publications by Jakob Richter
Visualization of predictions
In this post I want to shortly introduce you to the great visualization possibilities of mlr. Within the last months a lot of work has been put into that field. This post is not a tutorial but more a demonstration of how little code you have to write with mlr to get some nice plots showing the prediction behaviors for different learners. First w...
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Paper published: mlr – Machine Learning in R
We are happy to announce that we can finally answer the question on how to cite mlr properly in publications. Our paper on mlr has been published in the open-access Journal of Machine Learning Research (JMLR) and can be downloaded on the journal home page. The paper gives a brief overview of the features of mlr and also includes a comparison with...
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First release of mlrMBO – the toolbox for (Bayesian) Black-Box Optimization
We are happy to finally announce the first release of mlrMBO on cran after a quite long development time. For the theoretical background and a nearly complete overview of mlrMBOs capabilities you can check our paper on mlrMBO that we presubmitted to arxiv. The key features of mlrMBO are: Global optimization of expensive Black-Box functions. Muli...
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Use mlrMBO to optimize via command line
Many people who want to apply Bayesian optimization want to use it to optimize an algorithm that is not implemented in R but runs on the command line as a shell script or an executable. We recently published mlrMBO on CRAN. As a normal package it normally operates inside of R, but with this post I want to demonstrate how mlrMBO can be used to opt...
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Most Popular Learners in mlr
For the development of mlr as well as for an “machine learning expert” it can be handy to know what are the most popular learners used. Not necessarily to see, what are the top notch performing methods but to see what is used “out there” in the real world. Thanks to the nice little package cranlogs from metacran you can at least get a sl...
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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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