implemented xgdboost_regression

src/lib.rs

@@ -130,5 +130,6 @@ pub mod readers;
 pub mod svm;
 /// Supervised tree-based learning methods
 pub mod tree;
+pub mod xgboost;
 
 pub(crate) mod rand_custom;

src/xgboost/mod.rs

@@ -0,0 +1,16 @@
+//! # XGBoost
+//!
+//! XGBoost, which stands for Extreme Gradient Boosting, is a powerful and efficient implementation of the gradient boosting framework. Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
+//!
+//! The core idea of boosting is to build the model in a stage-wise fashion. It learns from its mistakes by sequentially adding new models that correct the errors of the previous ones. Unlike bagging, which trains models in parallel, boosting is a sequential process. Each new tree is fit on a modified version of the original data set, specifically focusing on the instances where the previous models performed poorly.
+//!
+//! XGBoost enhances this process through several key innovations. It employs a more regularized model formalization to control over-fitting, which gives it better performance. It also has a highly optimized and parallelized tree construction process, making it significantly faster and more scalable than traditional gradient boosting implementations.
+//!
+//! ## References:
+//!
+//! * "Elements of Statistical Learning", Hastie T., Tibshirani R., Friedman J., 10. Boosting and Additive Trees
+//! *  XGBoost: A Scalable Tree Boosting System, Chen T., Guestrin C.
+
+// xgboost implementation
+pub mod xgb_regressor;
+pub use xgb_regressor::{XGRegressor, XGRegressorParameters};