What does the term "overfitting" signify in the context of machine learning?

In the context of machine learning, "overfitting" refers to a situation where a model becomes too complex and learns not only the underlying patterns in the training data but also the noise and outliers that exist in that data. This can occur when the model has too many parameters relative to the amount of training data available, allowing it to essentially 'memorize' the training examples instead of generalizing well to unseen data.

When overfitting happens, the accuracy of the model may be very high on the training data because it has learned every detail, including the random fluctuations that do not represent true patterns. However, this often results in poor performance on new, unseen data, as the model fails to apply learned patterns effectively.

In summary, overfitting signifies that a model's complexity has led it to capture noise rather than generalizable trends, which hampers its performance on data it hasn't seen before.