feat: documents ensemble models

src/tree/decision_tree_regressor.rs

@@ -55,7 +55,8 @@
 //! * ["Classification and regression trees", Breiman, L, Friedman, J H, Olshen, R A, and Stone, C J, 1984](https://www.sciencebase.gov/catalog/item/545d07dfe4b0ba8303f728c1)
 //! * ["An Introduction to Statistical Learning", James G., Witten D., Hastie T., Tibshirani R., Chapter 8](http://faculty.marshall.usc.edu/gareth-james/ISL/)
 //!
-//! <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_CHTML"></script>
+//! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
+//! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 
 use std::collections::LinkedList;
 use std::default::Default;
@@ -187,7 +188,7 @@ impl<'a, T: RealNumber, M: Matrix<T>> NodeVisitor<'a, T, M> {
 }
 
 impl<T: RealNumber> DecisionTreeRegressor<T> {
-    /// Build a regression tree regressor from the training data.
+    /// Build a decision tree regressor from the training data.
     /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
     /// * `y` - the target values
     pub fn fit<M: Matrix<T>>(

src/tree/mod.rs

@@ -6,7 +6,7 @@
 //! and fit a simple prediction model within each region. In order to make a prediction for a given observation, \\(\hat{y}\\)
 //! decision tree typically use the mean or the mode of the training observations in the region \\(R_j\\) to which it belongs.
 //!
-//! Decision trees often does not deliver best prediction accuracy when compared to other supervised learning approaches, such as linear and logistic regression.
+//! 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.
@@ -16,7 +16,8 @@
 //! * ["Classification and regression trees", Breiman, L, Friedman, J H, Olshen, R A, and Stone, C J, 1984](https://www.sciencebase.gov/catalog/item/545d07dfe4b0ba8303f728c1)
 //! * ["An Introduction to Statistical Learning", James G., Witten D., Hastie T., Tibshirani R., Chapter 8](http://faculty.marshall.usc.edu/gareth-james/ISL/)
 //!
-//! <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_CHTML"></script>
+//! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
+//! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 
 /// Classification tree for dependent variables that take a finite number of unordered values.
 pub mod decision_tree_classifier;