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The performance of many machine learning algorithms depends on their hyperparameter settings.
The Choice of a Class Interval
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James Bergstra and Yoshua Bengio. 2012 · 2012
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Efficient and Robust Automated Machine Learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015 · 2015
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To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning. In 2015 International Joint Conference on Neural Networks (IJCNN)
Rafael G. Mantovani, Andre L. D. Rossi, Joaquin Vanschoren, Bernd Bischl, and Andre C. P. L. F. Carvalho. 2015 · 2015
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Equivalence Tests
Daniël Lakens. 2017 · 2017
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SciPy: Open source scientific tools for Python
Eric Jones, Travis Oliphant, Pearu Peterson, et al · 2018
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Tunability: Importance of Hyperparameters of Machine Learning Algorithms
Philipp Probst, Bernd Bischl, and Anne-Laure Boulesteix. 2018 · 2018
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Hyperparameter Importance Across Datasets
J. N. Van Rijn and F. Hutter. 2018 · 2018
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