Society of Actuaries PA Practice Exam Study Guide

Supervised learning is a machine learning method specifically designed for tasks where the goal is to learn a function that maps inputs to outputs based on labeled training data. In this context, each instance of input data is paired with a corresponding output label, allowing the model to learn the relationship between the two during the training process.

As the model is trained on this labeled dataset, it makes predictions or classifications on new, unseen data by applying the learned function to the input features. This method is fundamental for tasks like regression, where the outputs are continuous values, and classification, where outputs are discrete labels.

In contrast, other options do not involve this direct input-output function mapping. Unsupervised learning operates on datasets without labeled responses and focuses on identifying patterns or structures in the data, such as clustering or association. Hierarchical clustering is a specific unsupervised technique used to group data points without pre-defined labels, while dimensionality reduction refers to methods that reduce the number of features in a dataset, often to highlight important relationships or to prepare for supervised learning. These approaches do not aim to map inputs to specific outputs based on labels.