Spearmint is a Python-based open-source software package for performing Bayesian optimization, primarily aimed at automating and optimizing machine learning experiments. It is particularly suited for academic researchers and practitioners who wish to minimize experimental runs while tuning parameters to reach optimal results. Spearmint supports advanced features such as multi-task optimization, input warping, and handling unknown constraints, and outputs results for direct analysis or database access.
Visit Spearmint's official website for product details and getting started.
Comprehensive guide and API reference for using Spearmint for Bayesian optimization.
Engage with other users and developers, share experiences, and ask questions about Spearmint.
A collection of examples and tutorials to get started with Spearmint and understand its capabilities.