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

ABSTRACT: 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.
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.
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets
http://arxiv.org/abs/2008.13162v1