Bagging (Bootstrap Aggregating) and Boosting are both ensemble learning techniques that aim to improve the predictive performance of machine learning models by combining multiple base learners. However, they differ in their approach to training and how they leverage the base learners’ predictions to improve model performance. Bagging focuses on reducing variance, whereas Boosting focuses on…
XGBoost stands for eXtreme Gradient Boosting, and it’s an optimized and highly scalable implementation of the Gradient Boosting framework. Developed by Tianqi Chen and now maintained by the Distributed (Deep) Machine Learning Community, XGBoost has gained widespread popularity in machine learning competitions and real-world applications due to its efficiency, flexibility, and outstanding performance. XGBoost Parameters…
Gradient Boosting is another ensemble learning technique used for classification and regression tasks and has its own specific way of building the ensemble of weak learners. Here’s a brief overview of Gradient Boosting: Gradient Boosting typically produces more accurate models compared to AdaBoost but can be more computationally expensive and prone to overfitting, especially with…
AdaBoost (Adaptive Boosting) is a popular ensemble learning algorithm used for classification and regression tasks. It works by combining multiple weak learners (typically decision trees, often referred to as “stumps”) to create a strong learner. Here’s how it generally works: AdaBoost is effective because it focuses on improving the classification of difficult examples by giving…
