What is active learning?
A machine learning approach where the model actively selects the most informative data to learn from
active learning explained in plain English
Analogy
Imagine you're trying to learn a new language and you have a teacher who can correct your mistakes. Active learning is like having a student who can ask the teacher to correct the most confusing sentences first, rather than just listening to a random selection of sentences
Example
For example, a company might use active learning to build a model that can classify customer support emails as either positive or negative, by actively selecting the most uncertain emails for a human to label
How is active learning used?
Active learning is often used in situations where labeling data is expensive or time-consuming, such as in medical imaging or text classification
Common misconceptions about active learning
One common misconception is that active learning is only useful when there is a limited amount of labeled data, but it can also be useful when there is a large amount of unlabeled data
History
Active learning has its roots in the 1980s, but it has gained popularity in recent years with the development of more advanced machine learning algorithms
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