What is Extreme Gradient Boosting (XGBoost)?
Extreme Gradient Boosting, often abbreviated as XGBoost, is a popular ensemble learning algorithm known for its efficiency and effectiveness in classification and regression tasks. XGBoost belongs to the family of gradient boosting algorithms, which works by sequentially combining weak learners (typically decision trees) to create a strong learner. It minimizes a loss function by adding new models that predict the residuals or errors made by the existing models. It provides better performance compared to traditional boosting algorithms by incorporating regularization techniques and parallel processing.
Support Vector Machine vs Extreme Gradient Boosting
Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) are both powerful machine learning algorithms widely used for classification and regression tasks. They belong to different families of algorithms and have distinct characteristics in terms of their approach to learning, model type, and performance. In this article, we discuss about characteristics of SVM and XGBoost along with their differences and guidance on when to use SVM and XGBoost based on different scenarios.