Active Learning¶
Active Learning aims at creating training data for classification algorithms, in a very efficient manner, for cases in which a large amount of unlabeled data is available but labels are not. Labeling such data is usually time-consuming and expensive. To avoid having to label the full dataset, Active Learning selectively chooses data points that are assumed to improve the model. This is done iteratively, in a process that alternates between an algorithm selecting data to label, and a human annotator who assigns the true labels to given samples. The goal here is to maximize the quality of the model while keeping the annotation efforts at a minimum. A comprehensive introduction to Active Learning can be found in (Settles, 2010) [Set10].
