Which type of machine learning problem is focused on predicting a continuous value?

The focus of a regression problem is on predicting a continuous value, which makes it distinct from other types of machine learning problems. In regression tasks, the goal is to forecast a numeric outcome based on input features. For example, predicting house prices based on attributes like size, location, and the number of bedrooms is a typical regression scenario where the output is a continuous variable.

In contrast, classification problems deal with predicting discrete categories or classes rather than continuous values. For instance, determining whether an email is spam or not involves classifying it into one of two categories.

Clustering problems involve grouping similar data points into clusters based on their features. This method does not predict a specific outcome but rather identifies patterns or structures in the data, making it less relevant when focusing on predicting continuous values.

Anomaly detection is primarily concerned with identifying rare items or events that deviate significantly from the majority of the data. It does not focus on predicting a continuous metric but rather on flagging outliers or unusual behavior within the dataset.

Thus, regression problems are the appropriate choice when the objective is to predict a continuous outcome.