Press "Enter" to skip to content

A Conceptual Discussion of Active Learning

Kevin Jacobs teaches us to learn:

Active Learning is a method in which data is annotated in s smart way. With data annotation, you would normally get to see a randomly selected item which you need to label. This however can lead to a lot of repetition of similar items which you have to label. This is a waste of time. A better way would be to use Active Learning. For Active Learning, a batch of random items is selected first. Then, a lightweight classifier is used for evaluating the previously annotated data.

Basically, run your prediction mechanism, find the things about which the mechanism is least certain, and figure those out. Doing this reduces ambiguity and quickly leads to a better model.