Understanding the XGBoost Algorithm with Detailed Explanation and Python Implementation XGBoost, short for "Extreme Gradient Boosting", is a powerful algorithm widely used in machine learning, especially for regression and classification problems. It is known for delivering high performance and is frequently used in Kaggle competitions. In this article, we’ll explore XGBoost’s key features, a basic Python implementation, and a practical example using the Scikit-learn library. Key Features of XGBoost Boosting: Combines multiple weak learners (typically decision trees) sequentially to create a strong learner. Each tree corrects the errors of the previous one. Gradient Boosting: Adds trees based on the gradient of the loss function, optimizing using gradient descent. Regularization: Applies L1 and L2 regularization to control model complexity and prevent overfitting. Tree Pruning: Uses max depth pruning to reduce unnecessary complexity. Handling Missing Values: Aut...
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