minor fixes to doc

Author: Mec-iS

src/metrics/mod.rs

@@ -4,7 +4,7 @@
 //! In a feedback loop you build your model first, then you get feedback from metrics, improve it and repeat until your model achieve desirable performance.
 //! Evaluation metrics helps to explain the performance of a model and compare models based on an objective criterion.
 //!
-//! Choosing the right metric is crucial while evaluating machine learning models. In smartcore you will find metrics for these classes of ML models:
+//! Choosing the right metric is crucial while evaluating machine learning models. In `smartcore` you will find metrics for these classes of ML models:
 //!
 //! * [Classification metrics](struct.ClassificationMetrics.html)
 //! * [Regression metrics](struct.RegressionMetrics.html)

src/model_selection/mod.rs

@@ -7,7 +7,7 @@
 //! Splitting data into multiple subsets helps us to find the right combination of hyperparameters, estimate model performance and choose the right model for
 //! the data.
 //!
-//! In smartcore a random split into training and test sets can be quickly computed with the [train_test_split](./fn.train_test_split.html) helper function.
+//! In `smartcore` a random split into training and test sets can be quickly computed with the [train_test_split](./fn.train_test_split.html) helper function.
 //!
 //! ```
 //! use smartcore::linalg::basic::matrix::DenseMatrix;

src/neighbors/knn_classifier.rs

@@ -1,6 +1,6 @@
 //! # K Nearest Neighbors Classifier
 //!
-//! smartcore relies on 2 backend algorithms to speedup KNN queries:
+//! `smartcore` relies on 2 backend algorithms to speedup KNN queries:
 //! * [`LinearSearch`](../../algorithm/neighbour/linear_search/index.html)
 //! * [`CoverTree`](../../algorithm/neighbour/cover_tree/index.html)
 //!

src/svm/mod.rs

@@ -9,7 +9,7 @@
 //! SVM is memory efficient since it uses only a subset of training data to find a decision boundary. This subset is called support vectors.
 //!
 //! In SVM distance between a data point and the support vectors is defined by the kernel function.
-//! smartcore supports multiple kernel functions but you can always define a new kernel function by implementing the `Kernel` trait. Not all functions can be a kernel.
+//! `smartcore` supports multiple kernel functions but you can always define a new kernel function by implementing the `Kernel` trait. Not all functions can be a kernel.
 //! Building a new kernel requires a good mathematical understanding of the [Mercer theorem](https://en.wikipedia.org/wiki/Mercer%27s_theorem)
 //! that gives necessary and sufficient condition for a function to be a kernel function.
 //!

src/svm/svc.rs

@@ -20,7 +20,7 @@
 //!
 //! Where \\( m \\) is a number of training samples, \\( y_i \\) is a label value (either 1 or -1) and \\(\langle\vec{w}, \vec{x}_i \rangle + b\\) is a decision boundary.
 //!
-//! To solve this optimization problem, smartcore uses an [approximate SVM solver](https://leon.bottou.org/projects/lasvm).
+//! To solve this optimization problem, `smartcore` uses an [approximate SVM solver](https://leon.bottou.org/projects/lasvm).
 //! The optimizer reaches accuracies similar to that of a real SVM after performing two passes through the training examples. You can choose the number of passes
 //! through the data that the algorithm takes by changing the `epoch` parameter of the classifier.
 //!

src/tree/decision_tree_regressor.rs

@@ -11,7 +11,7 @@
 //!
 //! where \\(\hat{y}_{Rk}\\) is the mean response for the training observations withing region _k_.
 //!
-//! smartcore uses recursive binary splitting approach to build \\(R_1, R_2, ..., R_K\\) regions. The approach begins at the top of the tree and then successively splits the predictor space
+//! `smartcore` uses recursive binary splitting approach to build \\(R_1, R_2, ..., R_K\\) regions. The approach begins at the top of the tree and then successively splits the predictor space
 //! one predictor at a time. At each step of the tree-building process, the best split is made at that particular step, rather than looking ahead and picking a split that will lead to a better
 //! tree in some future step.
 //!