Large-Scale Benchmarking

MCML Authors

Marc Becker

→ Group Bernd Bischl
 Statistical Learning and Data Science

Abstract

In the field of machine learning, benchmark experiments are used to evaluate and compare the performance of algorithms. To draw robust conclusions, benchmark experiments often have to be ‘large-scale’, which means including many datasets, learners, and possibly measures. Finding datasets can be difficult and the choice of dataset impacts conclusions that can be drawn. Conducting large-scale benchmark experiments is also complex as they are usually computationally intensive. It is therefore common to make use of high-performance computing clusters to efficiently run the experiment. Finally once these experiments are run, analysis of experiments usually requires more than a single score from a given performance measure, and therefore statistical test are often employed.<br>This chapter introduces mlr3oml for interfacing the OpenML database for accessing data and tasks. It then continues by discussing how to run experiments on high-performance computing clusters using batchtools and mlr3batchmark. Finally, mlr3benchmark is introduced for statistical analysis including Friedman tests and critical difference diagrams.

article

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Applied Machine Learning Using mlr3 in R

IV.11. Jan. 2024.

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Authors

S. Fischer • M. Lang • M. Becker

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability