AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

Logistic regression is fundamentally designed for binary classification tasks where the goal is to predict the probability that a given input belongs to a particular class. The model outputs values that range between 0 and 1, corresponding to the likelihood of the input being in one of the classes, usually denoted as class 0 or class 1. This probabilistic output is what makes logistic regression particularly useful for tasks such as diagnosing diseases, determining credit risk, or any scenario where a yes/no decision is required.

The rationale for this design is grounded in the use of the logistic function (or sigmoid function), which transforms a linear combination of input features into a probability. Thus, the statement about measuring the probability of belonging to specific classes is fundamentally correct and captures the essence of what logistic regression aims to achieve.

The use of logistic regression goes beyond just offering classifications based on a numeric target or assessing the impact of features alone; it provides useful insights in the form of probabilities that can lead to better decision-making in various applications.