XGBoost | DataScienceBase

XGBoost

Learn about XGBoost, one of the most widely used gradient boosting libraries known for its speed and performance. XGBoost offers advanced features like regularization and handling missing values, making it versatile for various machine learning tasks. Understand how to leverage XGBoost's powerful capabilities in competitions and real-world applications across finance, healthcare, and more.

XGBoost Theory

Explore the theoretical foundations of XGBoost, focusing on boosting techniques and model optimization.

scikit-learn Example

Utilize scikit-learn to efficiently implement XGBoost and achieve high performance in your models.

TensorFlow Example

Learn how to implement XGBoost models using TensorFlow with practical examples.

PyTorch Example

Implement XGBoost in PyTorch, leveraging its power for advanced model training.

Common Mistakes & Best Practices

Avoid common pitfalls in XGBoost and learn best practices for tuning and optimization.

Comparison with Other Algorithms

Compare XGBoost with other machine learning algorithms to understand its advantages in different scenarios.