Active Learning vs Machine Learning

Active learning is a specialized approach within machine learning that focuses on efficiency. Instead of training a model with a massive dataset, active learning strategically selects the most valuable or informative data points to be labeled and used for training. This minimizes the need for extensive labeled data, which can often be costly, time-consuming, or difficult to obtain.

On the other hand, machine learning in its broader sense relies on using large datasets to train models to recognize patterns and make predictions. While this traditional approach is powerful, it may not always be practical when labeled data is limited. Active learning addresses this challenge by reducing data requirements while still achieving strong performance, making it especially useful in fields like healthcare, natural language processing, and image recognition.