Hyperopt: a Python library for model selection and hyperparameter optimization

Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of…

• OO
  Office of the Director

  Award: 0963668
• RI
  Rowland Institute at Harvard
• NS
  Natural Sciences and Engineering Research Council of Canada