LASSO, Ridge, and Support Vector Regression

Lasso, Ridge, and support vector regression (SVR) are machine learning algorithms used for regression tasks, but they differ in their approach and characteristics. Lasso and Ridge are regularization techniques used in conjunction with linear regression, whereas SVR is a fundamentally different algorithm that employs a kernel trick to discover nonlinear relationships. Lasso is useful for feature selection, model simplification, and situations where some predictors are suspected to be irrelevant. Ridge is suitable for situations with multicollinearity (highly correlated predictors) and for preventing overfitting by reducing the model's complexity without removing features. SVR is suitable for high-dimensional spaces and nonlinear data, but can be computationally expensive with large datasets. Key features of the Lasso regression algorithm (L1 regularization) and the Ridge regression algorithm (L2 regularization) are described. The elastic net regression algorithm is explained. After briefly discussing the SVR algorithm, a comparison is made between the Ridge, Lasso, and SVR algorithms.