Learning Multiple Defaults for Machine Learning Algorithms

Understand

The performance of modern machine learning methods highly depends on their hyperparameter configurations.

• One simple way of selecting a configuration is to use default settings, often proposed along with the publication and implementation of a new algorithm.
• Those default values are usually chosen in an ad-hoc manner to work good enough on a wide variety of datasets.
• To address this problem, different automatic hyperparameter configuration algorithms have been proposed, which select an optimal configuration per dataset.