Print Email Facebook Twitter Solving ML with ML: Effectiveness of the Metropolis-Hastings algorithm for synthesizing Machine Learning Pipelines Title Solving ML with ML: Effectiveness of the Metropolis-Hastings algorithm for synthesizing Machine Learning Pipelines Author Sheremet, Denys (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Dumančić, S. (mentor) Hinnerichs, T.R. (graduation committee) Tax, D.M.J. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-25 Abstract In AutoML, the search space of possible pipelines is often large and multidimensional. This makes it very important to use an efficient search algorithm. We measure the effectiveness of the Metropolis-Hastings algorithm (M-H) in a pipeline synthesis framework, when the search space is described by a context-free grammar. We also compare the performance of the M-H algorithm to other search algorithms. While AutoML frameworks use many different search algorithms, and comparisons between AutoML frameworks exist, this is the first paper that compares the performance of different search algorithms in the context of pipeline synthesis under equal conditions. We found that M-H is slightly outperformed by BFS2, the simplest possible search algorithm. We conclude that the datasets we use for evaluating the algorithms are too simple to meaningfully compare the performance of different search algorithms. We also conclude that for simple datasets, simple search algorithms work best. Subject AutoMLPipeline SynthesisMachine LearningAutomated Machine LearningProgram Synthesis To reference this document use: http://resolver.tudelft.nl/uuid:1d0c2a54-6e34-4727-8858-7c6a78968d22 Part of collection Student theses Document type bachelor thesis Rights © 2023 Denys Sheremet Files PDF CSE3000_Denys_Sheremet_Fi ... eport_.pdf 365.38 KB Close viewer /islandora/object/uuid:1d0c2a54-6e34-4727-8858-7c6a78968d22/datastream/OBJ/view