Auto-sklearn provides out-of-the-box supervised machine learning. Built around the scikit-learn machine learning library, auto-sklearn automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its hyperparameters. Thus, it frees the machine learning practitioner from these tedious tasks and allows her to focus on the real problem.

References in zbMATH (referenced in 19 articles )

Showing results 1 to 19 of 19.
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  1. Antonio, Candelieri: Sequential model based optimization of partially defined functions under unknown constraints (2021)
  2. Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, Wenwu Zhu: AutoGL: A Library for Automated Graph Learning (2021) arXiv
  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. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  5. 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)
  6. Bemporad, Alberto: Global optimization via inverse distance weighting and radial basis functions (2020)
  7. Sandeep Singh Sandha, Mohit Aggarwal, Igor Fedorov, Mani Srivastava: MANGO: A Python Library for Parallel Hyperparameter Tuning (2020) arXiv
  8. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)
  9. Xavier-Júnior, João C.; Freitas, Alex A.; Ludermir, Teresa B.; Feitosa-Neto, Antonino; Barreto, Cephas A. S.: An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results (2020)
  10. Lindauer, Marius; van Rijn, Jan N.; Kotthoff, Lars: The algorithm selection competitions 2015 and 2017 (2019)
  11. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)
  12. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  13. Haifeng Jin, Qingquan Song, Xia Hu: Auto-Keras: An Efficient Neural Architecture Search System (2018) arXiv
  14. Li, Lisha; Jamieson, Kevin; DeSalvo, Giulia; Rostamizadeh, Afshin; Talwalkar, Ameet: Hyperband: a novel bandit-based approach to hyperparameter optimization (2018)
  15. Melnikov, Vitalik; Hüllermeier, Eyke: On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis (2018)
  16. Mohr, Felix; Wever, Marcel; Hüllermeier, Eyke: ML-plan: automated machine learning via hierarchical planning (2018)
  17. Wistuba, Martin; Schilling, Nicolas; Schmidt-Thieme, Lars: Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2018)
  18. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  19. Bischl, Bernd; Kerschke, Pascal; Kotthoff, Lars; Lindauer, Marius; Malitsky, Yuri; Fréchette, Alexandre; Hoos, Holger; Hutter, Frank; Leyton-Brown, Kevin; Tierney, Kevin; Vanschoren, Joaquin: ASlib: a benchmark library for algorithm selection (2016)