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minor fix

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Lorenzo
2022-11-08 12:18:03 +00:00
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commit b71c7b49cb
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src/linear/ridge_regression.rs
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@@ -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. //! 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. //! 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, //! 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. //! but does not work for all data matrices. Unlike the Cholesky decomposition, all matrices have an SVD decomposition.
//! //!