Active Learning
Active Learning is a machine learning methodology where the algorithm selectively queries a human oracle (e.g., a user or expert) to label new data points, focusing on the most informative or uncertain instances to improve model performance with minimal labeled data. It is commonly used in scenarios where labeling data is expensive, time-consuming, or requires domain expertise, such as in medical imaging, natural language processing, or fraud detection. By iteratively selecting the most valuable data for annotation, it reduces the amount of labeled data needed compared to passive learning approaches.
Developers should learn and use Active Learning when working on machine learning projects with limited labeled datasets, as it optimizes the labeling effort and accelerates model training while maintaining high accuracy. It is particularly valuable in domains like healthcare, where expert annotation is costly, or in applications like sentiment analysis, where manual labeling of large text corpora is impractical. This methodology helps build robust models more efficiently by prioritizing data that maximizes learning gains.