Blog Archives

Stepwise Bayesian Optimization with mlrMBO

January 9, 2018
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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 following we will demonstrate this feature on...

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Parameter tuning with mlrHyperopt

July 18, 2017
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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. The only drawback and shortcoming of mlr...

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Most Popular Learners in mlr

March 29, 2017
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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...

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Use mlrMBO to optimize via command line

March 21, 2017
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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...

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First release of mlrMBO – the toolbox for (Bayesian) Black-Box Optimization

March 12, 2017
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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. Mulit-Criteria Optimization. Parallelization through multi-point...

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Paper published: mlr – Machine Learning in R

October 19, 2016
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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.

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Visualization of predictions

July 27, 2015
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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...

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