Six reasons why XGBoost is better than GBM | Rabi Singh

Six reasons why XGBoost is better than GBM

XGBoost (eXtreme Gradient Boosting) and GBM (Gradient Boosting Machine) are both popular machine-learning algorithms for classification and regression problems.

Here are some reasons why XGBoost is considered better than GBM:

1. Regularization: XGBoost has a regularization term in its objective function, which penalizes models for being too complex, thus reducing overfitting. GBM does not have this feature.

2. Parallel processing: XGBoost uses parallel processing to optimize its performance, making it faster than GBM.

3. Handling missing values: XGBoost can handle missing values by assigning them to the direction that best reduces the loss, whereas GBM cannot.

4. Flexibility: XGBoost allows users to define custom optimization objectives and evaluation metrics, making it more flexible than GBM.

5. Scalability: XGBoost can handle large datasets with millions of rows and thousands of columns, whereas GBM struggles with large datasets.

6. Tree pruning: XGBoost uses a more advanced tree pruning algorithm, which prunes trees in a bottom-up manner, while GBM prunes trees in a top-down manner.

Overall, XGBoost is considered a more powerful and efficient algorithm than GBM, particularly for large and complex datasets.

However, the choice between the two algorithms ultimately depends on the specific problem and the data at hand.