XGBoost
XGBoost is an optimized machine learning algorithm based on gradient-boosted decision trees, designed to build accurate predictive models by combining multiple decision trees sequentially.
What is XGBoost?
XGBoost, short for Extreme Gradient Boosting, builds a series of decision trees where each new tree focuses on correcting errors made by previous trees. It uses regularization and other optimization techniques to improve model performance, control overfitting, and efficiently handle large datasets.
Why is XGBoost Important?
XGBoost is known for strong predictive performance, efficient training, and flexibility across different types of structured data. It can handle complex relationships and provides useful features for controlling model complexity, making it a widely used algorithm for practical machine learning tasks.
Common use cases
XGBoost is commonly used for classification, regression, ranking, fraud detection, customer analytics, risk assessment, and predictive modeling on structured or tabular data.