Summary of Statistical Multicriteria Benchmarking Via the Gsd-front, by Christoph Jansen (1) et al.
Statistical Multicriteria Benchmarking via the GSD-Front
by Christoph Jansen, Georg Schollmeyer, Julian Rodemann, Hannah Blocher, Thomas Augustin
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes reliable methods for comparing various machine learning classifiers, which is crucial given the numerous proposals of such models. The reliability is broken down into three aspects: simultaneously evaluating different quality metrics, accounting for statistical uncertainty in benchmark suites, and verifying the robustness under small deviations in assumptions. To address these concerns, the authors propose using a generalized stochastic dominance ordering (GSD) to compare classifiers, as well as a consistent statistical estimator for the GSD-front and a test to determine whether a new classifier lies within the GSD-front of state-of-the-art models. The concepts are illustrated on the PMLB benchmark suite and the OpenML platform. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to compare different types of machine learning models, so we can decide which one works best for a particular job. The problem is that many people have developed their own ways of comparing these models, but they’re not all reliable or consistent. To fix this, the authors suggest using a special kind of ordering system called GSD-front, which takes into account different quality metrics and statistical uncertainty. They also propose a way to test whether a new model is better than existing ones. The ideas are demonstrated on two datasets: PMLB and OpenML. |
Keywords
» Artificial intelligence » Machine learning
