Your go-to interactive machine learning library
Vowpal Wabbit provides a fast, flexible, online, and active learning solution that empowers you to solve complex interactive machine learning problems.
What does Vowpal Wabbit do?
Vowpal Wabbit provides fast, efficient, and flexible online machine learning techniques for reinforcement learning, supervised learning, and more. It is influenced by an ecosystem of community contributions, academic research, and proven algorithms. Vowpal Wabbit is sponsored by Microsoft Research .
Optimize rewards with Contextual Bandits
Contextual Bandits are a form of reinforcement learning (RL). The vast majority of production real-world RL systems use contextual bandits techniques.
For an overview of a contextual bandit problem, including how to use Vowpal Wabbit in a CB setting with Python, read the contextual bandit tutorial.
Active learning enables a learner to choose which examples to label given what it knows and a source of unlabeled examples. Active learning is used to minimize the cost of labeling datasets.
Most learning algorithms require all data before they start learning. Vowpal Wabbit enables efficient learning from a continuously growing data source for situations where a problem changes or interactive learning is needed.
Vowpal Wabbit handles learning problems with any number of sparse features. It is the first published tera-scale learner.
- Agarwal, A., Chapelle, O., Dudı́k Miroslav, & Langford, J. (2011). A Reliable Effective Terascale Linear Learning System. CoRR, abs/1110.4198. Retrieved from http://arxiv.org/abs/1110.4198 Get .bib
Vowpal Wabbit’s input format is more flexible than many toolkits. This flexibility allows examples with features consisting of free form text and multiple feature sources to be explicitly represented within an example and used later.
Install Vowpal Wabbit
A vehicle for machine learning research
Vowpal Wabbit’s design is informed and influenced by advanced interactive and online learning research.
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