What is a characteristic of irreducible error in prediction models?

Irreducible error refers to the part of the error in a prediction model that cannot be reduced or eliminated through any improvements in the predictors or the model. This type of error is fundamentally attributed to the randomness and inherent variability present in the data or the underlying process being modeled. Since it captures the noise and unpredictability that cannot be controlled for, irreducible error is a central concept in understanding the limits of predictive performance.

In contrast, the other options suggest that irreducible error can be eliminated or compared in a quantifiable way, which does not accurately reflect its nature. For instance, eliminating it with better predictors is not feasible, as the irreducible error is tied to aspects of data randomness that cannot be fully predicted or accounted for. Hence, recognizing that this type of error always exists highlights the fundamental limits on the accuracy of any predictive model and emphasizes the importance of understanding the stochastic nature of the data at hand.