> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/decision-intelligence/active-learning-algorithms.md). # Active Learning Algorithms When labels arrive in large streams, the learner has to update as they come instead of waiting for a full dataset. The page starts, and for now ends, with the Passive Aggressive classifier for massive data streams such as Twitter-scale text. The same notes are in [Active Learning](/problem-framing/active-learning.md) and [Online Learning](/problem-framing/online-learning.md). ### Passive Aggressive classifier For a stream that never stops, the first algorithm to reach for is a cheap one. [The Passive Aggressive](https://www.quora.com/Classification-machine-learning-What-is-an-intuitive-explanation-of-the-Passive-Aggressive-classifier) (PA) algorithm is perfect for classifying massive streams of data (e.g. Twitter). It is easy to implement and very fast, but does not provide global guarantees like the support-vector machine (SVM). To see it worked on text, Victor Lavrenko's Text Classification 3: Passive Aggressive Algorithm is the [YouTube, seems like active learning in stream..?](https://www.youtube.com/watch?v=TJU8NfDdqNQ) lecture. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/decision-intelligence/active-learning-algorithms.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.