From fe07b8c726922582edf1889df69c65d7dc79bca4 Mon Sep 17 00:00:00 2001 From: Aman Dalmia <amandalmia18@gmail.com> Date: Mon, 13 Feb 2017 18:54:49 +0530 Subject: [PATCH] DOC: added explanation for LARS (#8310) --- doc/modules/linear_model.rst | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index 8b6c232597..887a590f23 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -270,7 +270,7 @@ Comparison with the regularization parameter of SVM ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The equivalence between ``alpha`` and the regularization parameter of SVM, -``C`` is given by ``alpha = 1 / C`` or ``alpha = 1 / (n_samples * C)``, +``C`` is given by ``alpha = 1 / C`` or ``alpha = 1 / (n_samples * C)``, depending on the estimator and the exact objective function optimized by the model. @@ -398,7 +398,11 @@ Least Angle Regression Least-angle regression (LARS) is a regression algorithm for high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain -Johnstone and Robert Tibshirani. +Johnstone and Robert Tibshirani. LARS is similar to forward stepwise +regression. At each step, it finds the predictor most correlated with the +response. When there are multiple predictors having equal correlation, instead +of continuing along the same predictor, it proceeds in a direction equiangular +between the predictors. The advantages of LARS are: -- GitLab