Rankings
Reloaded
An open-source toolkit for visualizing benchmarking results
Rankings Reloaded Mission
The mission of Rankings Reloaded is to offer an open-source toolkit for robust and accurate uncertainty analysis and visualization of algorithm performance. Rankings Reloaded enables researchers to conduct fair benchmarking by revealing each algorithm’s true strengths and weaknesses.
Benchmarking Pitfalls in Machine Learning
The rapidly evolving field of machine learning (ML) is marked by ever faster development of new algorithms. In light of this competition, robust and reliable validation of algorithm performance is becoming increasingly important. International benchmarking competitions (“challenges”) have become the gold standard for benchmarking in ML, but are subject to frequent flaws in analysis and reporting [1]. Rankings Reloaded was developed to address these issues and empower researchers to conduct meaningful performance comparisons, avoiding common pitfalls
The Rankings Reloaded Framework
Rankings Reloaded is a user-friendly, ready-to-use open source framework for comprehensive uncertainty analysis in algorithm benchmarking. Building upon challengeR [2], Rankings Reloaded helps researchers identify strengths and weaknesses of algorithms for both individual benchmarking experiments and large-scale challenges, supporting both single-task and multi-task scenarios. By eliminating the need for complex installations, Rankings Reloaded makes powerful analyses accessible to developers unfamiliar with the R language.
About Rankings Reloaded
Report generation in just 4 steps:
1. Upload Your Data
Upload your score data in CSV format (sample data). The data must contain results for every case (image).
2. Configure Ranking
Choose your ranking method from metric-based, case-based or significance ranking options.
3. Configure Uncertainty Analysis
Choose if you want to apply bootstrapping methods. It will be used to investigate the ranking uncertainty (recommended).
4. Generate the Report
Provide a few final details necessary for your report. Then you are ready to download it.
Core Funding
Rankings Reloaded Publication
"Rankings Reloaded" is based on the publication "Methods and Open Source Toolkit for Analyzing and Visualizing Challenge Results." The goal of the paper is to suggest methods and provide an open-source framework for systematically analyzing and visualizing benchmarking results, including ranking uncertainty analysis. This approach aims to offer valuable insights for challenge organizers, participants, and individual researchers, helping them understand algorithm performance and validate datasets more intuitively. The paper covers various analysis and visualization techniques.
Please cite our paper if you use our online tool. For more information:
