Which regression method is often used for modeling binary outcomes?

Logistic regression is specifically designed for modeling binary outcomes, making it the most appropriate choice for this scenario. In situations where the response variable is categorical with two possible outcomes (commonly represented as 0 and 1), logistic regression provides a way to estimate the probability that a given instance belongs to a particular category based on one or more predictor variables.

The key feature of logistic regression is the use of the logistic function, also known as the sigmoid function, which transforms the linear combination of inputs into a probability value that ranges between 0 and 1. This transformation is crucial because it allows for the output to be interpreted directly as a probability, facilitating the classification of data points into the binary categories.

Other regression methods do not serve this purpose effectively. For instance, linear regression is generally used for modeling continuous outcomes rather than binary ones, which can lead to predictions outside the 0-1 range, making it unsuitable for binary classification tasks. Ridge and Lasso regression are techniques used for regularization, helping to prevent overfitting in linear models, but they also do not cater specifically to binary outcomes like logistic regression does. These methodologies are enhancements to linear regression, not replacements for tasks involving binary responses.