Spearmint
A Bayesian optimization software package for automatically running experiments to minimize an objective in as few runs as possible.
What is Spearmint?
Spearmint is a software package for performing Bayesian optimization, a method for optimizing expensive black-box functions. It automates the process of running experiments by intelligently selecting parameter sets to evaluate, aiming to find the optimal configuration in as few iterations as possible. This is particularly useful for tasks like tuning hyperparameters in machine learning models where each evaluation is computationally costly.
Researchers, data scientists, and machine learning engineers who need to optimize complex, expensive functions, such as hyperparameters for neural networks or other iterative experimental processes.
Developers choose Spearmint because it implements state-of-the-art Bayesian optimization algorithms from peer-reviewed research, offers automated and efficient experiment management, and reduces the number of costly function evaluations needed to find optimal solutions compared to naive methods like grid search.
Overview
Spearmint Bayesian optimization codebase
Use Cases
Best For
- Hyperparameter tuning for machine learning models
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