When a model cannot capture the underlying trends in the data, it is said to be:

When a model cannot capture the underlying trends in the data, it is characterized as underfitting. This situation occurs when the model is too simplistic to adequately represent the complexity of the data it is trained on. Underfitting typically results from using models that do not have enough capacity (for example, a linear model for data that is better described by a polynomial or more complex structure) or from insufficient training (such as stopping training too early). As a result, the model fails to learn the relevant patterns, leading to poor performance on both training and unseen test data.

In contrast, overfitting occurs when a model learns the training data too well, including noise and outliers, which might hinder its ability to generalize to new data. Generalizing refers to how well a model performs on unseen data after being trained, while biasing pertains to systematic errors made by a model due to assumptions or limitations, which can contribute to underfitting scenarios but does not specifically define the model's incapacity to capture trends in the data. Understanding the distinction between underfitting and other concepts is crucial for developing effective machine learning models.