minor fix

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.
 //!