Articles by Jakob Richter

Stepwise Bayesian Optimization with mlrMBO

January 9, 2018 | Jakob Richter

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 ...
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Parameter tuning with mlrHyperopt

July 18, 2017 | Jakob Richter

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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Most Popular Learners in mlr

March 29, 2017 | Jakob Richter

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 ... [Read more...]

Use mlrMBO to optimize via command line

March 21, 2017 | Jakob Richter

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 ... [Read more...]

Visualization of predictions

July 27, 2015 | Jakob Richter

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 ...
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