From b71c7b49cb59d18d9ad4a97370832a4f96c9f82e Mon Sep 17 00:00:00 2001 From: Lorenzo Date: Tue, 8 Nov 2022 12:18:03 +0000 Subject: [PATCH] minor fix --- src/linear/ridge_regression.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/linear/ridge_regression.rs b/src/linear/ridge_regression.rs index e03948d..6bd5595 100644 --- a/src/linear/ridge_regression.rs +++ b/src/linear/ridge_regression.rs @@ -12,7 +12,7 @@ //! where \\(\alpha \geq 0\\) is a tuning parameter that controls strength of regularization. When \\(\alpha = 0\\) the penalty term has no effect, and ridge regression will produce the least squares estimates. //! However, as \\(\alpha \rightarrow \infty\\), the impact of the shrinkage penalty grows, and the ridge regression coefficient estimates will approach zero. //! -//! smartcore uses [SVD](../../linalg/svd/index.html) and [Cholesky](../../linalg/cholesky/index.html) matrix decomposition to find estimates of \\(\hat{\beta}\\). +//! `smartcore` uses [SVD](../../linalg/svd/index.html) and [Cholesky](../../linalg/cholesky/index.html) matrix decomposition to find estimates of \\(\hat{\beta}\\). //! The Cholesky decomposition is more computationally efficient and more numerically stable than calculating the normal equation directly, //! but does not work for all data matrices. Unlike the Cholesky decomposition, all matrices have an SVD decomposition. //!