Xgboost Regression Regularization. Oct 05 2018 There are two regularisation parameters available in XGBoost which are L1 Regularisation L1 regularisation is used in the regression technique called Lasso Least Absolute Shrinkage. L1 regularisation adds the absolute value of the coefficient as the regularisation term.
In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens. But xgboost is enabled with internal CV function well see below. Lets us understand the reason behind the good performance of XGboost - Regularization.
Standard GBM implementation has no regularization like XGBoost therefore it also helps to reduce overfitting.
This could be the average in the case of regression and 05 in the case of classification. In R we usually use external packages such as caret and mlr to obtain CV results. Here is an example of Regularization and base learners in XGBoost. XGboost is commonly used for supervised learning in machine learning.
