Active Learning: Optimizing Data Labeling Efforts

What is active learning?

Supervised learning models tend to improve their accuracy as they are exposed to more labeled data. The process of deciding which data to select for human annotation is where active learning comes into play.

Active learning is a machine learning approach that helps improve the efficiency and effectiveness of model training by intelligently selecting the most valuable data points for labeling.

Unlike traditional passive learning, where all available data is labeled before training, active learning actively identifies which data samples would be most informative to the model and prioritizes them for annotation by human experts or annotators.

Note: There’s no one-size-fits-all solution for making machine learning models more accurate. Neither a specific algorithm, model architecture nor a fixed set of parameters guarantees optimal performance across all scenarios and datasets. Instead, the choice of active learning strategy should be guided by the characteristics of your data and the specific task at hand.