Explanation:
Clustering.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", clustering is an unsupervised machine learning technique used to group data points into distinct segments or clusters based on shared characteristics.
Unlike supervised learning (classification or regression), clustering works with unlabeled data, discovering natural groupings without predefined outcomes.
In this question, Recency, Frequency, and Monetary (RFM) values are common marketing metrics used to evaluate customer behavior:
* Recency - how recently a customer made a purchase.
* Frequency - how often they make purchases.
* Monetary - how much money they spend.
Using RFM analysis, a company can segment its customers into groups such as "loyal," "occasional," or "at- risk" buyers. This segmentation process does not rely on predefined labels but rather discovers patterns within the data - which is the defining characteristic of clustering.
In the AI-900 context, clustering is described as a method that "groups items with similar features so that items in the same group are more similar to each other than to those in other groups." Common algorithms used include K-Means, Hierarchical Clustering, and DBSCAN, all available within Azure Machine Learning Designer and other Azure ML environments.
To clarify the incorrect options:
* Classification is supervised learning used to predict discrete categories (e.g., yes/no, spam/not spam).
* Regression predicts continuous numeric values (e.g., house prices).
* Regularization is a model optimization technique, not a type of machine learning.
Therefore, when businesses use RFM values to identify customer segments without labeled outcomes, this is an application of unsupervised learning through clustering.