IBM Data Science Practice Test 2025 - Free Data Science Practice Questions and Study Guide

Linear regression is a widely used algorithm specifically designed for regression tasks, making it the correct choice. In regression analysis, the goal is to predict a continuous output variable based on one or more input features. Linear regression achieves this by establishing a linear relationship between the dependent variable and the independent variables, using the method of least squares to minimize the difference between the predicted and actual outcomes.

The algorithm works by determining the best-fitting line through the data points in the feature space, as this line represents the predicted values of the response variable based on the input features. Its simplicity and interpretability make linear regression an appealing choice for many predictive modeling scenarios, especially when the relationships between variables can be assumed to be linear.

Other algorithms mentioned, although powerful, are typically associated with other tasks. For example, K-Nearest Neighbors can be used for both classification and regression but is not specifically designed for regression. Support Vector Machines can also be adapted for regression with a variant known as Support Vector Regression, though it is primarily recognized for classification tasks. Naive Bayes is fundamentally a classification algorithm that operates on the assumption of conditional independence between features, making it unsuitable for regression applications.