What is the engine behind Active Learning?

Active Learning in the context of data analysis and machine learning is primarily driven by Support Vector Machine (SVM) technology. SVMs are supervised learning models that analyze and classify data into different categories by finding the optimal hyperplane that best separates the data points in multi-dimensional space.

The concept of Active Learning involves selecting the most informative data points for training models. In this context, SVMs can significantly enhance the learning process by effectively deciding which instances to query next based on their uncertainties. This maximizes efficiency since only the most critical data points are labeled and included in the model training process, thereby improving model accuracy and reducing the need for extensive labeled datasets.

While the Relativity Algorithm is integrated within the Relativity platform for e-discovery and could have its implications within project management, it is not specifically the mechanism behind Active Learning. Similarly, the Classification Index and Search Terms Report serve different purposes within data management and do not directly drive the Active Learning process like SVM does.