In machine learning, what is overfitting?

Overfitting occurs when a machine learning model learns the training data too well, capturing noise and details that do not generalize to new, unseen data. This typically results in a model that is overly complex, with too many parameters related to the training data, making it perform exceptionally well on the training dataset while struggling with real-world data or validation sets. The key characteristic of an overfit model is that it has minimized the training error but has not achieved a good balance with the validation error, leading to poor predictive performance in practice.

The other options highlight various aspects of machine learning but do not capture the essence of overfitting. For example, enhancing training speed, balancing datasets, or phases in model evaluation relate to different dimensions of model training and performance but do not define the specific issue of overfitting itself. Understanding overfitting is crucial for developing robust models that generalize well beyond the training dataset.