What defines overfitting in machine learning?

Overfitting in machine learning occurs when a model learns not only the underlying patterns in the training data but also the noise and outliers to an excessive degree. This means that while the model performs exceptionally well on the training data, it struggles to generalize to new, unseen data. By effectively memorizing the details of the training set—including random fluctuations that do not represent true underlying patterns—the model loses its ability to predict accurately when applied in practice.

A model that generalizes well to new data is the opposite of overfitting. Validation performance on different datasets is a practice meant to assess model generalization and does not signify overfitting. Additionally, considering only bias in training data does not capture the essence of overfitting, which is more related to variance—how well the model adapts to and represents the specific nuances of the dataset it was trained on. Hence, the identification of capturing noise in choice B is what accurately describes overfitting.