Explanation:
"features."
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe fundamental principles of machine learning on Azure," in a machine learning model, the data used as inputs are known as features, while the data that represents the output or target prediction is known as the label.
Features are measurable attributes or properties of the data used by a model to learn patterns and make predictions. They are also referred to as independent variables because they influence the result that the model tries to predict. For example, in a machine learning model that predicts house prices:
* Features might include square footage, location, and number of bedrooms, while
* The label would be the house price (the value being predicted).
In the context of Azure Machine Learning, during model training, features are passed into the algorithm as input variables (X-values), and the label is the corresponding output (Y-value). The model then learns the relationship between the features and the label.
Let's review the incorrect options:
* Functions: These are mathematical operations or relationships used inside algorithms, not the input data itself.
* Labels: These are the outputs or results that the model predicts, not the inputs.
* Instances: These refer to individual data records or rows in the dataset, not the input fields themselves.
Hence, in any supervised or unsupervised learning process, the input data (independent variables) are called features, and the model uses them to predict labels (dependent variables).