What function aggregates the errors made by a model during training, measuring overall prediction error?

The function that aggregates the errors made by a model during training and measures the overall prediction error is known as the loss function. The loss function quantifies how well the model's predictions align with the actual outcomes. By calculating the difference between predicted values and actual values, the loss function provides a single scalar value that serves as feedback for optimizing the model during training.

By minimizing the value of the loss function through techniques like gradient descent, a model can learn and improve its accuracy over time. Consequently, it is essential in guiding the adjustments to the model’s weights during each training iteration, helping to fine-tune predictions.

The other options, while related to the performance evaluation of models, serve different purposes. Cross-validation is a technique used to assess how the results of a statistical analysis will generalize to an independent data set, rather than directly measuring prediction error. The activation function refers to the mechanism that determines whether a neuron should be activated or not based on its input, influencing which neurons fire in neural networks, but it does not aggregate errors. Thus, the loss function is specifically designed to measure and aggregate prediction errors, making it the most appropriate choice for the question.