What is the expected relevance rank score range for most documents in the early stages of an Active Learning project?

In the early stages of an Active Learning project, the relevance rank score typically falls within the range of 40 to 60. This is because Active Learning systems are in the process of being trained and refined. At the beginning, the model has not yet developed a strong understanding of the data and its context, so the scores reflect a relatively low confidence in the ranking of document relevance. The scores within this range indicate that these documents possess some relevant features but lack precision and certainty, as the algorithm is still learning from the data it processes.

As the Active Learning project progresses, refinement of the model through iterative training will likely lead to higher relevance scores as the system becomes more adept at identifying relevant documents based on the training it receives. Hence, the selection of the range between 40 and 60 accurately reflects the initial uncertainty and variability expected during the early phase of an Active Learning project.