Least Angle Regression (LARS)
A linear regression algorithm called Least Angle Regression (LARS) is intended for high-dimensional data. It effectively calculates a solution path as a function of the regularization parameter, demonstrating how regularization affects the coefficients in the model. The way LARS functions is by repeatedly choosing the predictor that has the strongest correlation with the answer but hasn’t been added to the active set. Following that, it proceeds in the direction that minimizes the angle formed by the residual and the current predictor. This process keeps going until the target number of features is attained. LARS provides a thorough understanding of feature importance and is especially helpful for datasets that have more predictors than observations.
Understanding LARS Lasso Regression
A regularization method called LARS Lasso (Least Angle Regression Lasso) is used in linear regression to decrease the number of features and enhance the model’s predictive ability. It is a variation on the Lasso (Least Absolute Shrinkage and Selection Operator) regression, in which certain regression coefficients shrink to zero as a result of penalizing the absolute values of the regression coefficients. By successfully eliminating unnecessary characteristics from the model, the data is represented in a way that is easier to understand and more economical.