Which Boosting Algorithm to Use?
Now it is a very basic question, if all of these machine learning algorithms are the best performing, fast, and gives higher accuracies, then which to use?
Well, the answer to these questions can not be a single boosting algorithm among them as all of them are the best-fit solution for a particular type of problem on which we are working.
For example, If you think that there is a need for regularization according to your dataset, then you can definitely use XGBoost, If you want to deal with categorical data, then CatBoost and LightGBM perform very well on those types of datasets. If you need more community support for the algorithm then use algorithms which was developed years back XGBoost or Gradient Boosting.
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.