Difference Between Boosting Algorithms
| Algorithms | Gradient Boosting | AdaBoost | XGBoost | CatBoost | LightGBM |
|---|---|---|---|---|---|
| Year | – | 1995 | 2014 | 2017 | 2017 |
| Handling Categorical Variables | May require preprocessing like one-hot encoding | No | NO | Automatically handles categorical variables | No |
| Speed/Scalability | Moderate | Fast | Fast | Moderate | Fast |
| Memory Usage | Moderate | Low | Moderate | High | Low |
| Regularization | NO | No | Yes | Yes | Yes |
| Parallel Processing | No | No | Yes | Yes | Yes |
| GPU Support | No | No | Yes | Yes | Yes |
| Feature Importance | Available | Available | Available | Available | Available |
GradientBoosting vs AdaBoost vs XGBoost vs CatBoost vs LightGBM
Boosting algorithms are one of the best-performing algorithms among all the other Machine Learning algorithms with the best performance and higher accuracies. All the boosting algorithms work on the basis of learning from the errors of the previous model trained and tried avoiding the same mistakes made by the previously trained weak learning algorithm.
It is also a big interview question that might be asked in data science interviews. In this article, we will be discussing the main difference between GradientBoosting, AdaBoost, XGBoost, CatBoost, and LightGBM algorithms, with their working mechanisms and their mathematics of them.