Which of the following is an example of using regression in machine learning?

Choosing to predict house prices based on attributes is an example of using regression in machine learning because regression techniques are specifically designed to estimate continuous outcomes. In this case, the attributes of the houses, such as size, location, number of bedrooms, and age, serve as independent variables that influence the dependent variable, which is the house price. The goal of a regression model in this context is to learn the relationship between these variables and accurately predict the price of a house given its attributes.

Regression models provide a way to quantify the relationship between inputs and a continuous output, making them well-suited for scenarios like pricing predictions, where the outcome is not discrete but ranges over a spectrum of values. In contrast, other options like classifying emails focus on categorical outcomes, grouping customers deals with clustering techniques rather than regression, and detecting anomalies involves unsupervised learning scenarios rather than predicting continuous values. Thus, the nature of the task in predicting house prices aligns perfectly with the principles of regression analysis.