What is the term for a machine learning model that has been trained on a large dataset and can be adapted for related tasks?

The term for a machine learning model that has been trained on a large dataset and can be adapted for related tasks is best described as a machine learning model. This term encompasses models that can leverage learned representations and insights from a significant amount of data to perform well not just on the specific task they were originally trained for, but also on similar or related tasks through techniques such as transfer learning. This adaptability is a core advantage of machine learning models, particularly in contexts where data for new tasks may be limited.

While the other options include terms that are components of machine learning, they lack the broader applicability that "machine learning model" denotes. For instance, an artificial intelligence model could refer to a wide range of systems, including those that do not use machine learning. A regression model specifically refers to a type of statistical model used for predicting continuous outcomes but may not adequately capture the adaptability for various tasks. A supervised learning classifier is a more specific category of models that require labeled data for training and might not encompass the full range of adaptability that machine learning models exhibit when trained on large datasets.