In machine learning, which of the following refers to a model's ability to generalize to unseen data?

The concept of a model's ability to generalize to unseen data is crucial in machine learning. Generalization refers to how well a trained model performs on new, unseen data that was not part of the training set. This is essential because the ultimate goal of a machine learning model is to make accurate predictions or classifications on fresh inputs, not just the examples it has already seen.

When a model is said to have good generalization, it indicates that it has learned the underlying patterns in the training data rather than just memorizing the specific details of that data. This ability allows the model to apply its learned knowledge effectively to predict or infer outcomes for new instances.

Other concepts related to model performance, such as overfitting and underfitting, are directly connected to generalization. Overfitting occurs when a model learns too much detail and noise from the training data, resulting in poor performance on new data. Underfitting, on the other hand, happens when a model is too simplistic to capture the underlying structure of the data, also leading to poor generalization. Model complexity refers to the capacity of the model to fit the training data, which can influence how well a model generalizes, but it does not directly define the ability to generalize itself