In the context of Salesforce, what are features defined as?

The correct answer is based on understanding the concept of features in the context of data science and machine learning as applied in Salesforce. Features are essentially the individual measurable properties or characteristics used for training algorithms. In Salesforce, when you refer to training data, it encompasses various attributes or factors that can influence the outcome of a predictive model.

Features provide the inputs that the model uses to make predictions or analyze data. For example, in a CRM context, features could include variables such as customer age, purchase history, or interaction frequency, which are crucial for building and training machine learning models. Therefore, identifying and understanding the relevant features in your training dataset directly impacts the effectiveness and accuracy of your predictions.

The other options do not accurately represent what features are in this context. Historical records refer to past data, predicted outcomes relate to the results that a model forecasts, and segments of the dataset describe divisions within the data rather than the characteristics themselves that help in modeling.