From 527477dea7577d8fd6be3b063cc9bfa37078b427 Mon Sep 17 00:00:00 2001
From: "Lorenzo (Mec-iS)" <tunedconsulting@gmail.com>
Date: Mon, 7 Nov 2022 13:00:51 +0000
Subject: [PATCH] minor fixes

---
 src/cluster/kmeans.rs  | 2 +-
 src/dataset/mod.rs     | 2 +-
 src/lib.rs             | 4 ++--
 src/numbers/realnum.rs | 2 +-
 src/tree/mod.rs        | 2 +-
 5 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/cluster/kmeans.rs b/src/cluster/kmeans.rs
index 4384ddb..c542ae2 100644
--- a/src/cluster/kmeans.rs
+++ b/src/cluster/kmeans.rs
@@ -11,7 +11,7 @@
 //! these re-calculated centroids becoming the new centers of their respective clusters. Next all instances of the training set are re-assigned to their closest cluster again.
 //! This iterative process continues until convergence is achieved and the clusters are considered settled.
 //!
-//! Initial choice of K data points is very important and has big effect on performance of the algorithm. smartcore uses k-means++ algorithm to initialize cluster centers.
+//! Initial choice of K data points is very important and has big effect on performance of the algorithm. `smartcore` uses k-means++ algorithm to initialize cluster centers.
 //!
 //! Example:
 //!
diff --git a/src/dataset/mod.rs b/src/dataset/mod.rs
index ac48bf8..855b288 100644
--- a/src/dataset/mod.rs
+++ b/src/dataset/mod.rs
@@ -1,6 +1,6 @@
 //! Datasets
 //!
-//! In this module you will find small datasets that are used in smartcore mostly for demonstration purposes.
+//! In this module you will find small datasets that are used in `smartcore` mostly for demonstration purposes.
 pub mod boston;
 pub mod breast_cancer;
 pub mod diabetes;
diff --git a/src/lib.rs b/src/lib.rs
index b06d668..03bfc03 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -75,7 +75,7 @@
 //! ```
 //!
 //! ## Overview
-//! 
+//!
 //! ### Supported algorithms
 //! All machine learning algorithms are grouped into these broad categories:
 //! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data.
@@ -86,7 +86,7 @@
 //! * [Nearest Neighbors](neighbors/index.html), K Nearest Neighbors for classification and regression
 //! * [Naive Bayes](naive_bayes/index.html), statistical classification technique based on Bayes Theorem
 //! * [SVM](svm/index.html), support vector machines
-//! 
+//!
 //! ### Linear Algebra traits system
 //! For an introduction to `smartcore`'s traits system see [this notebook](https://github.com/smartcorelib/smartcore-jupyter/blob/5523993c53c6ec1fd72eea130ef4e7883121c1ea/notebooks/01-A-little-bit-about-numbers.ipynb)
 
diff --git a/src/numbers/realnum.rs b/src/numbers/realnum.rs
index cb5336a..f4d9aec 100644
--- a/src/numbers/realnum.rs
+++ b/src/numbers/realnum.rs
@@ -1,5 +1,5 @@
 //! # Real Number
-//! Most algorithms in smartcore rely on basic linear algebra operations like dot product, matrix decomposition and other subroutines that are defined for a set of real numbers, ℝ.
+//! Most algorithms in `smartcore` rely on basic linear algebra operations like dot product, matrix decomposition and other subroutines that are defined for a set of real numbers, ℝ.
 //! This module defines real number and some useful functions that are used in [Linear Algebra](../../linalg/index.html) module.
 
 use num_traits::Float;
diff --git a/src/tree/mod.rs b/src/tree/mod.rs
index a1b82c8..340b0a8 100644
--- a/src/tree/mod.rs
+++ b/src/tree/mod.rs
@@ -9,7 +9,7 @@
 //! Decision trees suffer from high variance and often does not deliver best prediction accuracy when compared to other supervised learning approaches, such as linear and logistic regression.
 //! Hence some techniques such as [Random Forests](../ensemble/index.html) use more than one decision tree to improve performance of the algorithm.
 //!
-//! smartcore uses [CART](https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees_.28CART.29) learning technique to build both classification and regression trees.
+//! `smartcore` uses [CART](https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees_.28CART.29) learning technique to build both classification and regression trees.
 //!
 //! ## References:
 //!