Which term describes a method of processing that involves iteratively refining models?

The term that describes a method of processing involving iteratively refining models is incremental learning. This approach focuses on continuously updating a model as new data arrives, allowing the model to improve its predictions over time without needing to retrain from scratch. This is particularly advantageous in scenarios where data is generated in a stream or when it's impractical to have a complete dataset at all times.

Incremental learning adapts the model to new information, learning from each iteration, which makes it highly relevant in dynamic environments where the underlying data can change. This capability helps in maintaining model relevance and accuracy, making it an attractive method in machine learning practices.

In contrast, other terms mentioned do not capture this iterative and gradual refinement process. For example, in-depth learning typically refers to a more traditional approach involving deep learning techniques but does not inherently focus on incremental updates. Single-shot answer methods deliver a definitive output based on a single input without ongoing refinement. Lastly, sequential learning may imply a focus on the order of data but does not specifically emphasize the continuous refinement of a model as new data is introduced.