What key element must be balanced to achieve high performance in predictive modeling?

Achieving high performance in predictive modeling requires a careful balance between accuracy and reliability. Accuracy refers to how close a model's predictions are to the actual outcomes, indicating its effectiveness in making correct predictions. Reliability, on the other hand, pertains to the consistency of the model's predictions across different datasets or scenarios.

For a predictive model to perform well, it is not enough to simply achieve high accuracy on a training dataset; it must also demonstrate reliability when applied to new, unseen data. This ensures that the model is robust and can be trusted in making decisions based on its predictions. Thus, balancing these two elements is crucial for successful predictive modeling, as a model that is highly accurate but lacks reliability may lead to poor outcomes in practical applications.