What does underfitting refer to in machine learning?

Underfitting in machine learning occurs when a model fails to capture the underlying trend of the data, leading to poor performance not only on the training dataset but also when predicting new, unseen data. This typically arises when the model is too simple, meaning it doesn't have enough complexity to learn from the data effectively.

When we consider a model that cannot fit the training data, it indicates that there are patterns or structures within the data that the model is missing. Consequently, such a model struggles to generalize its learned patterns to new datasets, resulting in high bias. An example of this might be using a linear regression model to fit a dataset with a quadratic relationship, which would lead to substantial underfitting.

The other options pertain to different aspects of model performance. A model fitting the training data perfectly refers to overfitting, where the model captures noise rather than the true signal. A model that is too complex may indicate overfitting rather than underfitting, as complexity can lead to better training accuracy but poor generalization. An oversimplified model refers specifically to the scenario where important data features are ignored, leading to underfitting, but the broader definition best aligns with the choice that describes the inability to fit or generalize correctly