AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

The statement regarding linear regression and logistic regression that is true asserts that linear regression deals with continuous target variables while logistic regression is more suitable for categorical targets.

Linear regression is designed to predict an outcome that is continuous, such as predicting a person's weight based on their height. It assumes a linear relationship between the independent variables (predictors) and the continuous dependent variable (outcome).

In contrast, logistic regression is specifically used when the outcome variable is categorical, often binary (e.g., yes/no, success/failure). It models the probability of a certain class or event existing, thereby yielding a value between 0 and 1—indicative of the likelihood that a given input point belongs to a certain category.

This distinction between continuous and categorical target variables highlights the differing purposes and applications of the two regression types in statistical analysis and predictive modeling.