Description: The term ‘epochs’ in the context of training machine learning models refers to the number of complete passes through the training dataset. Each epoch means that the model has seen and processed each example in the dataset at least once. During each epoch, the model adjusts its internal parameters to minimize prediction error. This process is fundamental to learning, as it allows the model to learn patterns and features from the data. As the number of epochs increases, the model has more opportunities to learn, but there is also the risk of overfitting, where the model adapts too closely to the training data and loses generalization ability. Therefore, choosing the number of epochs is a critical aspect of model training, often determined through techniques like cross-validation. In summary, epochs are an essential component in the training cycle of machine learning models, directly influencing their performance and generalization capability.
