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Finding optimal hyperparameters for the machine learning algorithm can often significantly improve its performance.
Classification and regression by randomforest
A. Liaw and M. Wiener · 2002
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Interpretable meta-measure for model performance, 2020
A. Gosiewska, K. Woznica, and P. Biecek · 2006
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Regularization paths for generalized linear models via coordinate descent
J. Friedman, T. Hastie, and R. Tibshirani · 2010
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OpenML: Networked Science in Machine Learning
J. Vanschoren, J. N. van Rijn, B. Bischl, and L. Torgo · 2013
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An automatic benchmarking system
M. Edel, A. Soni, and R. R. Curtin · 2014
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An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage, 2014
M. R. Smith, A. White, C. Giraud-Carrier, and T. Martinez · 2014
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mlr: Machine learning in r
B. Bischl, M. Lang, L. Kotthoff, J. Schiffner, J. Richter, E. Studerus, G. Casalicchio, and Z. M. Jones · 2016
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Catboost: unbiased boosting with categorical features, 2017
L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin · 2017
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ranger: A fast implementation of Random Forests for High Dimensional Data in C++ and R
M. N. Wright and A. Ziegler · 2017
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Automatic Exploration of Machine Learning Experiments on OpenML, 2018
D. Kühn, P. Probst, J. Thomas, and B. Bischl · 2018
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Tunability: Importance of Hyperparameters of Machine Learning Algorithms, 2018
P. Probst, B. Bischl, and A.-L. Boulesteix · 2018
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