What is an epoch in the context of model training?

In the context of model training, an epoch refers to a complete pass through the entire training dataset. During an epoch, the learning algorithm utilizes every training sample to update the model's parameters. This process is fundamental in the training of machine learning models, particularly in neural networks, where multiple epochs are often required to ensure that the model learns the underlying patterns in the data effectively.

Training a model typically involves multiple epochs to allow the algorithm to make incremental updates and improve its performance over time. The number of epochs can greatly influence the final performance of the model, striking a balance between underfitting and overfitting. Hence, understanding the concept of an epoch is crucial for managing the training process effectively and achieving optimal model performance.