What do attributes (or features) contain in a model?

In the context of data models, attributes or features refer to the individual measurable properties or characteristics of the data used in the training of the model. These features are essential, as they serve as the input variables that the model evaluates to learn patterns and make predictions.

These attributes can include various types of information that contribute to the analytics process, such as numerical values, categorical data, or text inputs. They provide the model with the necessary information to establish relationships or trends within the data.

The other options do not accurately represent the role of attributes. The predicted outcomes refer to the output of the model after evaluating input features. Constant parameters are specific values in the model that do not change during the training process, while the final results of the analysis are a summary or conclusion drawn from the model's predictions rather than the data itself. Therefore, the option that correctly identifies what attributes contain within a model is the one that emphasizes their role as the input variables being evaluated.