Advanced GBDT Algorithms
Advanced GBDT Algorithms
less than a minute
Advanced GBDT Algorithms
🔴 XGBoost (Extreme Gradient Boosting)
🔵 LightGBM (Light Gradient Boosting Machine)
⚫️ CatBoost (Categorical Boosting)
🔵 LightGBM (Light Gradient Boosting Machine)
⚫️ CatBoost (Categorical Boosting)
XGBoost (Extreme Gradient Boosting)
- An optimized and highly efficient implementation of gradient boosting.
- Widely used in competitive data science (like Kaggle) due to its speed and performance.
Note: Research project developed by Tianqi Chen during his doctoral studies at the University of Washington.
LightGBM (Light Gradient Boosting Machine)
- Developed by Microsoft, this framework is designed for high speed and efficiency with large datasets.
- It grows trees leaf-wise rather than level-wise and uses Gradient-based One-Side Sampling (GOSS) to speed up the finding of optimal split points.
CatBoost (Categorical Boosting)
Developed by Yandex, this algorithm is specifically optimized for handling ‘categorical’ features without requiring extensive preprocessing (such as, one-hot encoding).
End of Section