This table summarizes the key differences and strengths of each Gradient Boosting framework.
| Feature/Aspect | LightGBM (LGBM) | XGBoost | CatBoost |
|---|---|---|---|
| Tree Growth Strategy | Leaf-wise growth, leading to deeper trees and potentially better accuracy. | Level-wise growth, resulting in more balanced trees. | Ordered boosting, reducing overfitting and improving generalization. |
| Speed and Memory | High speed and low memory usage, especially with large datasets. | Balanced speed and accuracy with robust regularization options. | Competitive performance with minimal hyperparameter tuning. |
| Handling Categorical Features | Requires preprocessing (e.g., label encoding). | Requires preprocessing of categorical features. | Natively handles categorical features without preprocessing. |
| Regularisation | Supports regularization but not as robust as XGBoost. | Strong regularization options (L1 and L2) to prevent overfitting. | Utilizes techniques like ordered boosting to mitigate overfitting. |
| Use Cases | Ideal for large datasets and when computational efficiency is a priority. | Suitable for structured data and tabular datasets; widely used in competitions. | Useful for datasets with many categorical features and missing values. |
| Performance | Fast training and efficient on large datasets. | Accurate and flexible, often used in competitions. | Provides competitive performance, especially with categorical data. |