How many documents with both positive and negative choices are needed for an active learning model to build?

An active learning model typically requires a substantial amount of data to effectively identify and learn the underlying patterns in the dataset. Having at least five documents for both positive and negative choices strikes a balance between providing enough examples for the model to learn from without overwhelming it with complexity.

With five examples of each category, the model can better capture the nuances and variations of the data, which is key to improving its accuracy and reducing uncertainty over time. This amount allows the model to make informed decisions about which documents to seek further input on, thus enhancing the learning process through an iterative cycle of training and evaluating.

In contrast, fewer examples may not provide sufficient diversity or representation, leading to a less robust model that fails to generalize well to new data. Therefore, having at least five documents each for positive and negative choices is crucial in building a reliable active learning model.