What is overfitting in relation to machine learning?

Overfitting in machine learning occurs when a model learns not just the underlying patterns in the training data, but also the noise and random fluctuations that do not generalize to unseen data. This typically happens when a model is overly complex relative to the amount of training data available, allowing it to fit the training set very closely. As a result, while the model may perform exceptionally well on the training data, its ability to generalize to new, unseen data diminishes significantly.

In this context, learning noise refers to the model's tendency to latch onto irrelevant details rather than focusing on the foundational structure or patterns in the data that would be indicative of future observations. This makes the model less effective for making predictions on real-world data since it does not adequately capture the true relationship between input variables and the expected output.

The other options do not accurately depict the phenomenon of overfitting. For instance, the first option relates to a failure to recognize patterns, which is more about underfitting than overfitting. Meanwhile, data preprocessing techniques and methods for expanding datasets are strategies aimed at improving model performance rather than describing overfitting itself.