What type of regression does the LARS Node perform?

The LARS (Least Angle Regression) Node performs least angle regressions, which is particularly advantageous when dealing with high-dimensional datasets. This technique is designed to efficiently produce a sequence of coefficient estimates, allowing for both variable selection and regularization. It operates under the premise of starting with all coefficients at zero and incrementally adding predictor variables into the model based on their correlations with the response variable.

Least angle regression is beneficial because it can handle situations where the number of predictors is much larger than the number of observations, making it suitable for many contemporary applications in predictive modeling. By selecting variables in a way that takes into account their correlations, LARS provides a comprehensive approach to regression analysis while maintaining model interpretability and performance.

The other forms of regression mentioned serve different purposes. Ridge regression focuses on regularization to prevent overfitting by adding a penalty related to the size of the coefficients. Logistic regression is used for binary classification problems rather than continuous outcome predictions. Multiple linear regression models a relationship between two or more predictors and a continuous response variable, but does not incorporate the specialized variable selection benefits inherent in least angle regression. Thus, the LARS node is specifically designed for least angle regressions, making this the correct choice.