{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119720"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119720","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A framework to search for machine learning pipelines","abstract":"In this thesis, we present DeepMining, a framework to search for machine learning pipelines. The high-level goal of DeepMining is to solve the pipeline search problem: given a problem and a dataset, find the pipeline best-suited to solve that problem. The DeepMining platform serves as a testbed for developers to experiment with different methods of computing and evaluating machine learning pipelines. Specifically, developers have autonomy over how to evaluate different configurations in parallel, score a pipeline given a dataset and hyperparameter configuration, and efficiently search over the pipeline space. 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DeepMining was designed with modularity and extensibility in mind: developers can easily implement new search algorithms, scoring functions, and computation frameworks. At the same time, users can switch between these modules with minimal effort.","abstract_has_math":false,"creators":["Ravikumar, Akshay"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Kalyan Veeramachaneni."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-22T22:21:19Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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