What is a common output for numeric prediction AI systems?

In the context of numeric prediction AI systems, the common output is exact numbers. These systems are designed to analyze data and make predictions about continuous outcomes. For example, a numeric prediction model might predict the future sales figures for a product, which would be expressed in precise numerical terms. This allows businesses to make informed financial decisions based on the estimated quantities provided by the model.

The other output options, while relevant in different types of AI applications, do not pertain specifically to the numeric prediction domain. Boolean values typically denote true or false outcomes and are more suited for binary classification tasks. Classification labels refer to discrete categories assigned to inputs in a classification problem rather than numeric values. Uncertainty ranges may indicate the degree of confidence in a prediction but do not provide specific predicted outcomes, making them less applicable as common outputs in numeric predictions where exact numbers are critical for precision.