Fine-Tuning XGBoost: 17 Hyperparameters & How to Tune Them

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XGBoost is a popular and powerful gradient boosting library, and it comes with a variety of hyperparameters that can be tuned to optimize model performance. Here’s a list of some important XGBoost hyperparameters and a brief explanation of how to tune them:

I. 10 Most Common XGBoost Hyperparameters:

1. Learning Rate (eta or learning_rate):

• Description: Controls the contribution of each tree to the final prediction. Lower values make the algorithm more robust but require more trees.
• Tuning: Typically set between 0.01 and 0.3. Use a lower value for more conservative boosting.

2. Number of Trees (n_estimators):

• Description: The number of boosting rounds or trees to build.
• Tuning: Generally, a higher number of trees improves performance, but it comes with increased computation time. Use cross-validation to find an optimal value.

3. Maximum Depth of a Tree (max_depth):

• Description: Maximum depth of a tree, which controls the complexity of the individual trees.
• Tuning: Tune along with min_child_weight. Start with a small…