Which of the following best describes a requirement for labels in a regression model?

In a regression model, the primary focus is on predicting a continuous output or numerical value based on input features. Therefore, the requirement for labels is that they must be numeric. This is because regression tasks aim to find relationships between input variables and a continuous outcome, allowing for predictions of future values based on the trends found in the training data.

When dealing with regression, the model learns from the numerical label (or output) associated with the input data, enabling it to understand patterns and correlations that can be used for prediction. Using anything other than numeric labels, such as categorical or textual data, would not fit the framework of regression, as these types of labels are typically associated with classification tasks instead.