OpenML

R package OpenML. Exploring Machine Learning Better, Together. ’OpenML.org’ is an online machine learning platform where researchers can automatically share data, machine learning tasks and experiments and organize them online to work and collaborate more effectively. We provide a R interface to the OpenML REST API in order to download and upload data sets, tasks, flows and runs, see <http://www.openml.org/guide> for more information.


References in zbMATH (referenced in 45 articles , 1 standard article )

Showing results 1 to 20 of 45.
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  1. Hornung, Roman; Boulesteix, Anne-Laure: Interaction forests: identifying and exploiting interpretable quantitative and qualitative interaction effects (2022)
  2. Barella, Victor H.; Garcia, Luís P. F.; de Souto, Marcilio C. P.; Lorena, Ana C.; de Carvalho, André C. P. L. F.: Assessing the data complexity of imbalanced datasets (2021)
  3. Feurer, Matthias; van Rijn, Jan N.; Kadra, Arlind; Gijsbers, Pieter; Mallik, Neeratyoy; Ravi, Sahithya; Müller, Andreas; Vanschoren, Joaquin; Hutter, Frank: OpenML-Python: an extensible Python API for OpenML (2021)
  4. Jomaa, Hadi S.; Schmidt-Thieme, Lars; Grabocka, Josif: Dataset2Vec: learning dataset meta-features (2021)
  5. Jonathan Bac, Evgeny M. Mirkes, Alexander N. Gorban, Ivan Tyukin, Andrei Zinovyev: Scikit-dimension: a Python package for intrinsic dimension estimation (2021) arXiv
  6. Kottke, Daniel; Herde, Marek; Sandrock, Christoph; Huseljic, Denis; Krempl, Georg; Sick, Bernhard: Toward optimal probabilistic active learning using a Bayesian approach (2021)
  7. Lienen, Julian; Hüllermeier, Eyke: Instance weighting through data imprecisiation (2021)
  8. Northcutt, Curtis G.; Jiang, Lu; Chuang, Isaac L.: Confident learning: estimating uncertainty in dataset labels (2021)
  9. Shaker, Ammar; Hüllermeier, Eyke: TSK-Streams: learning TSK fuzzy systems for regression on data streams (2021)
  10. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  11. Alcobaça, Edesio; Siqueira, Felipe; Rivolli, Adriano; Garcia, Luís P. F.; Oliva, Jefferson T.; de Carvalho, André C. P. L. F.: MFE: towards reproducible meta-feature extraction (2020)
  12. Barbiero, Pietro; Ciravegna, Gabriele; Cirrincione, Giansalvo; Tonda, Alberto; Squillero, Giovanni: Generating neural archetypes to instruct fast and interpretable decisions (2020)
  13. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  14. Chaabane, Ikram; Guermazi, Radhouane; Hammami, Mohamed: Enhancing techniques for learning decision trees from imbalanced data (2020)
  15. Kolb, Samuel; Teso, Stefano; Dries, Anton; De Raedt, Luc: Predictive spreadsheet autocompletion with constraints (2020)
  16. Mentch, Lucas; Zhou, Siyu: Randomization as regularization: a degrees of freedom explanation for random forest success (2020)
  17. Pimenta, Cristiano G.; de Sá, Alex G. C.; Ochoa, Gabriela; Pappa, Gisele L.: Fitness landscape analysis of automated machine learning search spaces (2020)
  18. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  19. Dorie, Vincent; Hill, Jennifer; Shalit, Uri; Scott, Marc; Cervone, Dan: Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition (2019)
  20. Ferone, Alessio; Maratea, Antonio: Integrating rough set principles in the graded possibilistic clustering (2019)

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