vowpal_wabbit
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
Vowpal Wabbit is a fast online machine learning library that trains models one example at a time without loading full datasets into memory, with built-in support for contextual bandit and reinforcement learning algorithms.
Vowpal Wabbit is a machine learning tool that has been around for many years and is known for being unusually fast. Machine learning usually means teaching a computer to recognize patterns by feeding it lots of examples. Most tools do this in large batches: collect a dataset, load it all into memory, train a model. Vowpal Wabbit works differently. It learns online, meaning it processes one example at a time and updates its understanding immediately, without needing to hold the whole dataset in memory at once. This makes it practical even when your data is larger than what fits on a computer.
The system was built with speed and scale in mind. It uses an approach called sparse gradient descent, which is a mathematical method for quickly moving toward better predictions after each new piece of data. The memory it uses stays roughly constant no matter how many examples you train on, because it uses a trick called hashing to keep the set of features it tracks from growing without bound.
Vowpal Wabbit can take in data in a fairly open format. Text features do not need to be pre-processed into numbers before being fed in. The system handles this internally by treating text as a bag of individual words. Features can also be grouped into namespaces, and pairs of feature groups can be combined automatically so the model can detect interactions between them, which is useful for tasks like ranking search results.
One area where Vowpal Wabbit has a particularly strong focus is reinforcement learning and what are called contextual bandit algorithms. Contextual bandits describe a type of problem where a system must choose between several actions based on the current situation and then observe only partial feedback: it sees whether the action it chose worked, but not how other choices would have performed. Vowpal Wabbit has several algorithms for this built in.
The code is written in C++ for performance and also has Python bindings for those who prefer working in Python. Installation guides and tutorials are available on the project wiki.
Where it fits
- Train a machine learning model on a dataset too large to fit in memory using Vowpal Wabbit's online learning approach.
- Build a contextual bandit recommendation system that learns which action works best based on the current situation and partial feedback.
- Use Vowpal Wabbit's Python bindings to add fast online learning to a data science or production machine learning pipeline.