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Title: Supplementary Material for Efficient and Robust Automated Machine Learning

Citation Type: Miscellaneous

Publication Year: 2015

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Abstract: Our auto-sklearn framework contains 15 base classifiers from scikit learn (out of which exactly one is chosen at each point during the optimization process). The 15 algorithms can generally be separated into 7 categories: generalized linear models (2 algorithms), support vector machines (2), discriminant analysis (2), nearest neighbors (1), nave Bayes (3), decision trees (1) and ensemble methods (4). A complete list of the algorithms is given in Table 1a in the main paper. While an in-depth description of each algorithm is out of the scope of this paper we want to give a brief description of each category and highlight complementary strengths of algorithms within one category.

Url: http://aad.informatik.uni-freiburg.de/papers/15-NIPS-auto-sklearn-supplementary.pdf

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Authors: Feurer, Matthias; Klein, Aaron; Eggensperger, Katharina; Springenberg, Jost T.; Blum, Manuel; Huter, Frank

Publisher: University of Freiburg, Germany

Data Collections: IPUMS USA

Topics: Other

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