Active learning is a machine learning approach that actively selects and queries the most informative instances from a large pool of unlabeled data.

Active learning is a subfield of machine learning where the model interacts with a human expert to obtain labels

The goal of active learning is to reduce the amount of labeled data needed for training

It is particularly useful when labeling large amounts of data is time-consuming, costly, or impractical.

Active learning algorithms typically use a query strategy to select the most informative instances.

The success of active learning heavily relies on the selection of an appropriate query strategy

Active learning is an iterative process where the model is trained on the labeled instances selected during each query cycle.

Active learning can be applied to various machine learning tasks, including classification, regression and clustering.