# MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets

arXiv: https://arxiv.org/abs/2008.13162 · HTML: https://mlsanity.com/paper/2008.13162/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV](https://mlsanity.com/paper/2201.11815/) (2022)

## Abstract (arXiv)

Finding optimal hyperparameters for the machine learning algorithm can often significantly improve its performance. But how to choose them in a time-efficient way? In this paper we present the protocol of generating benchmark data describing the performance of different ML algorithms with different hyperparameter configurations. Data collected in this way is used to study the factors influencing the algorithm's performance. This collection was prepared for the purposes of the study presented in the EPP study. We tested algorithms performance on dense grid of hyperparameters. Tested datasets and hyperparameters were chosen before any algorithm has run and were not changed. This is a different approach than the one usually used in hyperparameter tuning, where the selection of candidate hyperparameters depends on the results obtained previously. However, such selection allows for systematic analysis of performance sensitivity from individual hyperparameters. This resulted in a comprehensive dataset of such benchmarks that we would like to share. We hope, that computed and collected result may be helpful for other researchers. This paper describes the way data was collected. Here you can find benchmarks of 7 popular machine learning algorithms on 39 OpenML datasets. The detailed data forming this benchmark are available at: https://www.kaggle.com/mi2datalab/mementoml.