📜 Add docs and example for RandomForest type alias.

Author: ladezai

algorithms/linfa-ensemble/src/lib.rs

@@ -5,12 +5,19 @@
 //!
 //! ## Bootstrap Aggregation (aka Bagging)
 //!
-//! A typical example of ensemble method is Bootstrapo AGgregation, which combines the predictions of
+//! A typical example of ensemble method is Bootstrap Aggregation, which combines the predictions of
 //! several decision trees (see `linfa-trees`) trained on different samples subset of the training dataset.
 //!
+//! ## Random Forest
+//! 
+//! A special case of Bootstrap Aggregation using decision trees (see `linfa-trees`) with random feature 
+//! selection. A typical number of random prediction to be selected is $\sqrt{p}$ with $p$ being
+//! the number of available features.
+//!
 //! ## Reference
 //!
 //! * [Scikit-Learn User Guide](https://scikit-learn.org/stable/modules/ensemble.html)
+//! * [An Introduction to Statistical Learning](https://www.statlearning.com/)
 //!
 //! ## Example
 //!
@@ -32,15 +39,44 @@
 //!
 //! // Train the model on the iris dataset
 //! let bagging_model = EnsembleLearnerParams::new(DecisionTree::params())
-//!     .ensemble_size(100)
-//!     .bootstrap_proportion(0.7)
+//!     .ensemble_size(100)        // Number of Decision Tree to fit
+//!     .bootstrap_proportion(0.7) // Select only 70% of the data via bootstrap
 //!     .fit(&train)
 //!     .unwrap();
 //!
 //! // Make predictions on the test set
 //! let predictions = bagging_model.predict(&test);
 //! ```
 //!
+//! This example shows how to train a Random Forest model using 100 decision trees,
+//! each trained on 70% of the training data (bootstrap sampling) and using only 
+//! 30% of the available features.
+//!
+//! ```no_run
+//! use linfa::prelude::{Fit, Predict};
+//! use linfa_ensemble::RandomForestParams;
+//! use linfa_trees::DecisionTree;
+//! use ndarray_rand::rand::SeedableRng;
+//! use rand::rngs::SmallRng;
+//!
+//! // Load Iris dataset
+//! let mut rng = SmallRng::seed_from_u64(42);
+//! let (train, test) = linfa_datasets::iris()
+//!     .shuffle(&mut rng)
+//!     .split_with_ratio(0.8);
+//!
+//! // Train the model on the iris dataset
+//! let bagging_model = RandomForestParams::new(DecisionTree::params())
+//!     .ensemble_size(100)        // Number of Decision Tree to fit
+//!     .bootstrap_proportion(0.7) // Select only 70% of the data via bootstrap
+//!     .feature_proportion(0.3)   // Select only 30% of the feature
+//!     .fit(&train)
+//!     .unwrap();
+//!
+//! // Make predictions on the test set
+//! let predictions = bagging_model.predict(&test);
+//! ```
+
 mod algorithm;
 mod hyperparams;