What does generalization in machine learning refer to?

Generalization in machine learning refers to the ability of a model to apply learned patterns from the training data to new, unseen data. This is a critical aspect of model performance since the ultimate goal is not only to fit the training data accurately but also to effectively perform on data that the model has not encountered before. A well-generalized model can predict well on diverse datasets and avoids overfitting, where it performs exceptionally on training data but poorly on new data.

The other options highlight different aspects of machine learning or its processes. The accuracy of a model on training data measures how well the model learns the specifics of the training set, but does not indicate whether it will perform well on new data. Improving model performance is an ongoing pursuit in machine learning but does not directly define generalization. Lastly, confining a model to old data characterizes overfitting, which prevents effective generalization. Thus, generalization is essential as it encapsulates the model’s strength in applying insights beyond its training environment.