Dynamic model: Difference between revisions

Machine learning is an ever-evolving field that utilizes mathematical algorithms and statistical models to empower computer systems to learn from data and make decisions. A dynamic model in machine learning refers to a type of model that can adjust its behavior over time in response to changes in its environment or new information.

Dynamic models are especially beneficial in situations where the environment or data being used to train a model are constantly shifting. In this article, we'll cover more about dynamic models in detail - their key characteristics, how they operate, and some of the common types used in machine learning applications.