Scikit-learn: Machine learning in Python F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... the Journal of machine Learning research 12, 2825-2830, 2011 | 100477 | 2011 |
API design for machine learning software: experiences from the scikit-learn project L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... arXiv preprint arXiv:1309.0238, 2013 | 3684 | 2013 |
Scikit‐learn: Machine learning in python Fabian F Pedregosa Journal of machine learning research 12, 2825, 2011 | 2196 | 2011 |
Cross-language text classification using structural correspondence learning P Prettenhofer, B Stein Proceedings of the 48th annual meeting of the association for computational …, 2010 | 410 | 2010 |
Intrinsic plagiarism analysis B Stein, N Lipka, P Prettenhofer Language Resources and Evaluation 45, 63-82, 2011 | 230 | 2011 |
Scikit-learn: Machine learning in python journal of machine learning research F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... Journal of machine learning research 12, 2825-2830, 2011 | 211 | 2011 |
Duchesnay F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... E.: Scikit-learn: Machine learning in Python. JMLR 12, 2825-2830, 2011 | 111 | 2011 |
Cross-lingual adaptation using structural correspondence learning P Prettenhofer, B Stein ACM Transactions on Intelligent Systems and Technology (TIST) 3 (1), 1-22, 2011 | 107 | 2011 |
Gradient boosted regression trees in scikit-learn P Prettenhofer, G Louppe PyData 2014, 2014 | 94 | 2014 |
Scikit-learn: machine learning in Python. arXiv 2012 F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... arXiv preprint arXiv:1201.0490, 0 | 61 | |
and J. Vanderplas F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel Scikit-learn: Machine learning in Python. The Journal of machine Learning …, 2011 | 58 | 2011 |
Scikit-learn: Machine learning in Python L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... Journal of machine learning research 12 (85), 2825-2830, 2011 | 57 | 2011 |
Different degrees of explicitness in intentional artifacts-studying user goals in a large search query log M Strohmaier, P Prettenhofer, M Lux Proceedings of the CSKGOI 8, 2008 | 20 | 2008 |
scikit-learn/scikit-learn: Scikit-learn 0.22. 1 O Grisel, A Mueller, A Gramfort, G Louppe, P Prettenhofer, M Blondel, ... Zenodo, 2020 | 16 | 2020 |
Efficient statement identification for automatic market forecasting H Wachsmuth, P Prettenhofer, B Stein Proceedings of the 23rd International Conference on Computational …, 2010 | 12 | 2010 |
Acquiring explicit user goals from search query logs M Strohmaier, P Prettenhofer, M Kröll 2008 IEEE/WIC/ACM International Conference on Web Intelligence and …, 2008 | 11 | 2008 |
Dubourg VJJomlr F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... Scikit-learn: Machine learning in Python 12, 2825-30, 2011 | 8 | 2011 |
scikit-learn/scikit-learn: scikit-learn 1.0. 1 O Grisel, A Mueller, A Gramfort, G Louppe, P Prettenhofer, M Blondel, ... Zenodo, 2022 | 6* | 2022 |
Forecasting daily solar energy production using robust regression techniques G Louppe, P Prettenhofer 94th American Meteorological Society Annual Meeting, 2014 | 2 | 2014 |
Logistic regression F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... J. Mach. Learn. Res 12, 2825-2830, 2011 | 2 | 2011 |