OpenML
Ensembles of classifiers are among the best performing classifiers available in many data mining applications. Rather than training one classifier, multiple classifiers are trained, and their…
60 datasets, 60 tasks, 6 flows, 389 runs
Feature selection can be of value to classification for a variety of reasons. Real world data sets can be rife with irrelevant features, especially if the data was not gather specifically for the…
394 datasets, 394 tasks, 24 flows, 9454 runs
Benchmarking in Machine Learning is often much more difficult than it seems, and hard to reproduce. This study is a new approach to do a collaborative, in-depth benchmarking of algorithms, and allows…
101 datasets, 101 tasks, 374 flows, 1290 runs
Almost every form of statistical and machine learning method has been applied to learning QSARs at one time or another: linear regression, decision trees, neural networks, nearest-neighbour methods,…
16941 datasets, 0 tasks, 0 flows, 24034 runs
The work will be submitted to ECML-PKDD2016
0 datasets, 0 tasks, 0 flows, 0 runs
Ensembles of classifiers are among the best performing classifiers available in many data mining applications. However, most ensembles developed specifically for the dynamic data stream setting rely…
0 datasets, 62 tasks, 13 flows, 805 runs
Example of collaborative research conducted by means of OpenML NB:
2 datasets, 0 tasks, 0 flows, 0 runs
how should I proceed? [![run at everware](https://img.shields.io/badge/run…
0 datasets, 0 tasks, 0 flows, 0 runs
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
In this study, we investigate and summarize the performance of a wide range of ML algorithms (using its default hyper-parameter values) on a wide range of OpenML classifications tasks. This will yield…
414 datasets, 425 tasks, 104 flows, 17652 runs
To see what a study can do
0 datasets, 0 tasks, 0 flows, 0 runs
This study compares the local and global feature selection strategy on multilabel classification transformation methods
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…
1086 datasets, 1081 tasks, 1 flows, 0 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, 39 tasks, 53 flows, 9627 runs
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