Which function is typically used for continuous data classification?

Logistic regression is specifically designed for binary classification problems, where the outcome is categorical but can be interpreted in relation to continuous predictors. It estimates probabilities that represent the likelihood of a particular class being true, based on a linear combination of input features.

In a typical classification scenario, especially if dealing with continuous input data, logistic regression transforms this data to predict discrete outcomes, thereby allowing continuous data to be effectively classified into distinct categories. Its mathematical foundation also supports the interpretation of relationships between predictors and the binary response variable.

While decision trees and SVMs also handle classification tasks, they are more flexible in the types of data and structures they can handle, but the formality and interpretation associated with logistic regression make it a standard choice when dealing with continuous data as predictors. K-means clustering, on the other hand, is primarily used for unsupervised clustering and does not classify data points into predefined categories based on a dependent variable.