How does XGBoost grow trees differently from GBM?

XGBoost (Extreme Gradient Boosting) and Gradient Boosting Machine (GBM) are both popular machine learning algorithms used for supervised learning tasks. While both algorithms are based on the concept of boosting, they grow trees differently which leads to differences in performance and efficiency.

Feature Importance Calculation

One key difference between XGBoost and GBM is how they calculate feature importance. XGBoost uses the concept of gradient boosting to update weights of features based on their importance during tree construction. This allows XGBoost to prioritize features that have a significant impact on the model’s performance. On the other hand, GBM uses the residual error to determine feature importance which may not always capture the true importance of features.

Regularization

XGBoost also incorporates regularization techniques to prevent overfitting during tree construction. By adding penalties to the tree weights, XGBoost is able to control the complexity of the trees and improve generalization performance. In contrast, GBM does not have built-in regularization which can lead to overfitting on the training data.

Tree Pruning

Another difference between XGBoost and GBM is their approach to tree pruning. XGBoost uses a depth-first approach to prune trees which allows for more efficient tree growth and reduces the risk of overfitting. GBM, on the other hand, uses a depth-first approach which can lead to deeper trees and overfitting on the training data.

Conclusion

In conclusion, XGBoost and GBM are both powerful machine learning algorithms that utilize boosting to improve model performance. However, the way they grow trees and handle features can lead to differences in performance and efficiency. XGBoost’s use of gradient boosting, regularization, and efficient tree pruning techniques make it a popular choice for many machine learning tasks.