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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates\054 Inc\056)
/Language (en\055US)
/Created (2015)
/EventType (Poster)
/Description-Abstract (The success of machine learning in a broad range of applications has led to an ever\055growing demand for machine learning systems that can be used off the shelf by non\055experts\056 To be effective in practice\054 such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand\054 and also set their respective hyperparameters\056 Recent work has started to tackle this automated machine learning \050AutoML\051 problem with the help of efficient Bayesian optimization methods\056 In this work we introduce a robust new AutoML system based on scikit\055learn \050using 15 classifiers\054 14 feature preprocessing methods\054 and 4 data preprocessing methods\054 giving rise to a structured hypothesis space with 110 hyperparameters\051\056 This system\054 which we dub auto\055sklearn\054 improves on existing AutoML methods by automatically taking into account past performance on similar datasets\054 and by constructing ensembles from the models evaluated during the optimization\056 Our system won the first phase of the ongoing ChaLearn AutoML challenge\054 and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML\056 We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of auto\055sklearn\056)
/Producer (PyPDF2)
/Title (Efficient and Robust Automated Machine Learning)
/Date (2015)
/ModDate (D\07220160130183403\05508\04700\047)
/Published (2015)
/Type (Conference Proceedings)
/firstpage (2944)
/Book (Advances in Neural Information Processing Systems 28)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (C\056 Cortes and N\056D\056 Lawrence and D\056D\056 Lee and M\056 Sugiyama and R\056 Garnett)
/Author (Matthias Feurer\054 Aaron Klein\054 Katharina Eggensperger\054 Jost Springenberg\054 Manuel Blum\054 Frank Hutter)
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