Which algorithms are commonly used for regression tasks?

The choice highlighting linear regression, decision trees, and support vector regression is accurate because these algorithms are specifically designed to tackle regression tasks, which focus on predicting continuous outcomes based on input features.

Linear regression is the most straightforward method for establishing a relationship between the dependent variable and one or more independent variables by fitting a straight line to the data. This technique is highly interpretable and works well when the relationship between the variables is linear.

Decision trees operate by splitting the data into branches based on feature values, allowing for a non-linear approach to regression. They are versatile and can model complex relationships in the data, making them useful when the assumption of linearity is not present.

Support vector regression is a variant of support vector machines that is adapted for regression problems. It aims to find a function that deviates from the actual observed targets by a value no greater than a specified margin, thus allowing for flexibility and generalization in capturing patterns in the data.

The other options do not accurately represent algorithms used for regression tasks. Classification trees and clustering algorithms are primarily concerned with classification and grouping tasks, while genetic algorithms are optimization techniques rather than regression methods. The option mentioning only linear and logistic regression is limiting, as it excludes other effective regression techniques that are widely used.