According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of common machine learning types”, the term features refers to the input variables or independent variables used by a machine learning model to make predictions. These are the measurable properties or attributes of the data that influence the output (target) value.

In a supervised learning process, data is typically divided into two parts:

Features → The input variables used by the model to learn patterns (e.g., customer age, income, credit score).

Label (Target) → The outcome or value the model is trying to predict (e.g., whether a loan will be approved or the amount of a house price).

During training, the model uses the features to understand how input data correlates with the target output. Once trained, the model applies the same learned relationships to predict outcomes for new, unseen data using only the features.

For example:

In a regression model predicting house prices, features might include square footage, number of bedrooms, and location.

In a classification model predicting loan approval, features might include applicant income, credit score, and debt ratio.

To contrast with other options:

Dependent variables (or labels) are the outcomes the model predicts.

Identifiers (like customer IDs) are unique values that do not help the model learn relationships and are typically excluded from features.

Labels are the target outputs, not the inputs.

Therefore, in Azure Machine Learning and AI-900 terminology, data values used to make a prediction are called “features.”