The new BigML release brings Fusions to our Machine Learning platform, the new modeling capability that combines multiple models to achieve better results. A Fusion combines different supervised models (models, ensembles, logistic regressions, and deepnets) and aggregates their predictions to balance out the individual weaknesses of single models.
Since yesterday, on July 12, 2018, Fusions are available from the BigML Dashboard, API, and WhizzML, and they follow the same principle as ensembles, where the combination of multiple models often provides better performance than any of the individual components. All these details, along with the new and more complete text analysis options, are explained in the official launch webinar. You can watch it anytime on the BigML YouTube channel.
For further learning on Fusions and other new features, please visit our release page, where you will find:
- The slides used during the webinar.
- The detailed documentation to learn how to use Fusions with the BigML Dashboard and the BigML API.
- The series of six blog posts that gradually explain Fusions to give you a detailed perspective of what’s behind this new capability. We start with an introductory post that explains the basic concepts, followed by several use cases to understand how to put Fusions to use, and then three more posts on how to use Fusions through the BigML Dashboard, API, WhizzML and Python Bindings. Finally, we complete this series with a technical view of how Fusions work behind the scenes.
- An extra section with a blog post and documentation on the new text analysis enhancements released.
Thanks for your watching the webinar, for your support, and your nice feedback! For more queries or comments, please contact the BigML Team at email@example.com.