Active Learning

Definition of Active Learning

Active Learning is a type of machine learning where the algorithm actively chooses which data points to use in order to learn from.

What is Active Learning used for?

Active Learning is a subset of Machine learning used to improve the accuracy of supervised learning by allowing the machine to learn from human feedback. It is a type of semi-supervised learning which involves humans in the loop, where humans are actively involved in training and developing algorithms. Active Learning allows machines to receive feedback from humans during an iterative process, ultimately improving its algorithm’s performance.

Active Learning typically requires fewer labeled data than traditional supervised learning methods, making it more cost effective and faster for companies to use in their production environment. This approach can be used for a variety of tasks including classification, regression, clustering, and optimization. It can also be employed in different settings such as batch or streaming mode depending on the particular task and data at hand. For example, if there is a large dataset with many unlabeled samples that need to be classified into different groups or categories, active learning may be used by allowing humans to interact with the system as it progresses through each iteration. The human feedback helps inform the machine as to what classes are most likely present within each sample and this information is then incorporated back into the model so that it can better identify similar patterns in subsequent iterations. In addition to improving accuracy, Active Learning also reduces training time since there is no need for labeling all the data before training begins.

Overall, Active Learning provides a powerful tool for developing supervised models with less amount of labeled data compared to traditional methods while still increasing accuracy and reducing training time. It can be used across a variety of tasks such as classification and regression with both batch or streaming modes available depending on the task requirements.