We investigate the performance of a wide range of classification algorithms on a wide range of datasets to better understand when they perform well and when they don't. This will yield a meta-dataset…
512 datasets, 514 tasks, 63 flows, 91425 runs
One of the challenges in Machine Learning to find a classifier and parameter settings that work well on a given dataset. Evaluating all possible combinations typically takes too much time, hence many…
0 datasets, 0 tasks, 0 flows, 0 runs
The task of Quantitative Structure Activity Relationship (QSAR) Learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the…
0 datasets, 0 tasks, 0 flows, 0 runs
Paper on OpenML R library. Includes a case study on bagging vs forests
0 datasets, 0 tasks, 0 flows, 0 runs
A small study of algorithms on datasets provided by the students.
0 datasets, 0 tasks, 0 flows, 0 runs
[Sport Data Valley](https://www.sportinnovator.nl/sport-data-valley) is a Dutch initiative to collect, share and analyse datasets on sports and exercise.…
0 datasets, 0 tasks, 0 flows, 0 runs
Meta Album is a meta-dataset created for few-shot learning, meta-learning, continual learning, AutoML, and more. The Micro version is meant for quick experimentation. It only contains 20 randomly…
30 datasets, 30 tasks, 0 flows, 0 runs
Meta Album is a meta-dataset created for few-shot learning, meta-learning, continual learning, AutoML, and more. The Mini version contains 40 randomly selected examples for each class (hence the…
30 datasets, 30 tasks, 0 flows, 0 runs
Meta Album is a meta-dataset created for few-shot learning, meta-learning, continual learning, AutoML, and more. The Extended version contains the full datasets. Learn more about Meta-Album at…
27 datasets, 27 tasks, 0 flows, 0 runs
a study with no runs attached
0 datasets, 0 tasks, 0 flows, 0 runs