What term describes unique combinations of factors in AI models?

The term that accurately describes unique combinations of factors in AI models is "features." In the context of AI and machine learning, features refer to individual measurable properties or characteristics used to make predictions. Features are the input variables from which models learn patterns and relationships in the data, thereby helping in accurate decision-making.

Each feature can be thought of as a specific piece of information that distinguishes one data point from another. When multiple features are combined, they create a unique representation of data points within the model, allowing the algorithm to capture complex relationships.

In contrast, the other terms such as layers and nodes typically refer to the structure and architecture of neural networks, while datasets pertain to the collection of data being used in training or evaluation, rather than the specific attributes within that data.