Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", a regression model is a type of supervised machine learning model that is used to predict continuous numeric values based on one or more input variables (features).
In supervised learning, models are trained using labeled data, where each input record has a known target value (label). For regression specifically, the label represents a numeric quantity - such as price, age, temperature, or sales figures. The model learns to approximate a function that maps input variables to numeric outputs.
For example:
* Predicting house prices based on size, location, and number of rooms.
* Predicting monthly sales revenue from marketing spend and seasonality.
* Forecasting temperature based on historical weather data.
In Azure Machine Learning Designer and AutoML, when building a regression model, the label column must therefore be of numeric data type (integer or float). If the label were categorical (for example, "yes/no" or
"approved/denied"), the appropriate model type would be classification, not regression.
To contrast with other options:
* Boolean - used in binary classification problems (true/false outcomes).
* Datetime - used for time series forecasting, not standard regression labels.
* Text - used as input features for NLP models, not as regression targets.
Hence, when configuring a regression task in Azure, ensuring the target variable (label) is numeric is a fundamental requirement. The model's performance metrics-such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R²-also rely on numeric computations.