How are Yes-and-No predictions typically presented in AI systems?

Yes-and-No predictions in AI systems are typically represented as probabilities or scores because this approach provides a quantitative measure of certainty regarding the prediction outcome. By using probabilities, AI models can convey how confident they are in a particular prediction being a "Yes" or "No." For instance, a model might predict that there is a 70% chance of an event occurring (Yes) and a 30% chance of it not occurring (No). This scoring system allows users to make more informed decisions based on the degree of confidence rather than a binary outcome alone.

In contrast, textual outputs and visual graphs can sometimes accompany the results for better interpretation but are not the standard method of presenting Yes-and-No predictions. Direct answers may indicate a straightforward Yes or No response but lack the nuance that probabilities provide in understanding the model's certainty. Hence, the use of probabilities or scores is essential for effectively communicating the AI system's insights.