# Active learning (machine learning)

> Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources when necessary. In statistics […]

**Active learning** is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources when necessary.  In statistics literature, it is sometimes also called optimal experimental design. The information source is also called *teacher* or *oracle*.

There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels. This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. With this approach, there is a risk that the algorithm is overwhelmed by uninformative examples.  Recent developments are dedicated to multi-label active learning, hybrid active learning and active learning in a single-pass (on-line) context, combining concepts

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*Source: [Wikipedia](https://en.wikipedia.org/wiki/Active_learning_%28machine_learning%29)*

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- **URL:** https://wpsearchai.com/active-learning-machine-learning/
- **Published:** 2026-01-28T18:47:17+00:00
- **Modified:** 2026-01-28T18:47:17+00:00
- **Author:** admin
- **Categories:** Machine learning