What does "overfitting" in machine learning imply?

Overfitting in machine learning occurs when a model learns not only the underlying patterns in the training data but also the noise and outliers. This results in an overly complex model that captures all the specific details of the training data, leading to high accuracy during training. However, this complexity makes the model less effective when it encounters new, unseen data, as it cannot generalize well.

Therefore, a model that excels on training data but performs poorly on new data exemplifies overfitting. The model has tailored itself too closely to the training examples, failing to recognize broader patterns that apply more generally to data outside of the training set. This distinction between training and testing performance is critical in machine learning, emphasizing the importance of creating models that strike a balance between fitting the training data and being robust enough to make accurate predictions on new data.