Dynamic model: Difference between revisions

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. The dynamic model is frequently or even continuously [[retrained]]. Dynamic models are especially beneficial in situations where the environment or data being used to train a model is constantly shifting.

*[[Adaptability]]: Dynamic models are capable of adapting to changes in their environment or new information that is presented. This means they can modify their behavior and predictions in real-time based on the most up-to-date data available.

*[[Feedback]]: Dynamic models often include feedback mechanisms that enable them to learn from errors and improve performance over time. This feedback can come from various sources such as user [[input]], sensor [[data]], or other types of external signals.

*[[Statefulness]]: Dynamic models typically maintain an internal state that reflects their current understanding of the environment. This state can be updated as new information is received, enabling the model to continuously adjust its predictions and behavior in response.

*[[Complexity]]: Dynamic models tend to be more intricate than other types of models due to the need to incorporate feedback and maintain an internal state. While this complexity makes them harder to train and optimize, it also allows them to perform well across a variety of environments.