XGBoost Algorithm

Overview

XGBoost (eXtreme Gradient Boosting) is an advanced implementation of gradient boosting that's known for its high performance and accuracy in predictive modeling tasks.

How It Works

1. Sequential Building: Builds trees one at a time
2. Error Correction: Each new tree corrects errors from previous trees
3. Gradient Descent: Uses gradient descent to minimize errors
4. Regularization: Includes L1 and L2 regularization to prevent overfitting

Advantages

• High Accuracy: Often achieves state-of-the-art results
• Speed: Optimized for fast computation
• Regularization: Built-in regularization prevents overfitting
• Handles Missing Values: Automatically learns how to handle missing data
• Parallel Processing: Can utilize multiple CPU cores

When to Use XGBoost

• You need the best possible accuracy
• Your dataset has complex patterns
• You have sufficient computational resources
• You want to minimize overfitting

Hyperparameters in CMMI-DCC

• Learning Rate: Step size shrinkage (0.01 to 0.3)
• Max Depth: Maximum tree depth (3 to 10)
• Number of Estimators: Number of boosting rounds (100 to 1000)
• Subsample: Fraction of samples used per tree (0.5 to 1.0)

Related Algorithms

• Random Forest: Another ensemble method
• Gradient Boosting: The underlying technique