How many documents need to be coded for the classifier model to start making its predictions?

The chosen answer indicates that the classifier model requires a minimum of one document labeled as positive and one labeled as negative to start making its predictions. This is essential as classifiers operate based on supervised learning principles, where the model learns to differentiate between two classes based on the training data provided.

Having at least one example from each category allows the model to establish a baseline understanding of what constitutes a positive or negative document. This foundational data enables the model to recognize patterns and make informed predictions about new, unseen documents.

While having a larger dataset (like 50 or 100 positive and negative examples) might improve the performance and accuracy of the classifier, it is not strictly necessary for the model to begin its predictive function. Therefore, the requirement for just one positive and one negative document is the minimum threshold for starting the prediction process.