AI Engineering Degree 2025 – 400 Free Practice Questions to Pass the Exam

Supervised learning is a category of machine learning where the model is trained using labeled data. This means that the input data is paired with the correct output, allowing the model to learn the mapping between inputs and outputs. The techniques that fall under supervised learning primarily involve Regression and Classification.

Regression techniques are used when the output variable is continuous, which means it can take on any value within a range. This is often applied in scenarios such as predicting house prices based on various features.

Classification techniques, on the other hand, are used when the output variable is categorical, meaning it can take on discrete classes. This is typically applied in scenarios like spam detection in emails, where the model classifies emails as either "spam" or "not spam."

In contrast, the other options do not fit within the realm of supervised learning. Clustering and Association are techniques utilized in unsupervised learning, where data is analyzed without labeled outputs. Dimensionality Reduction focuses on reducing the number of features in the dataset without necessarily mapping inputs to outputs, and Data Cleaning and Preparation are vital preprocessing steps that are not specific learning techniques. Therefore, the techniques classified under supervised learning are indeed Regression and Classification.