Glossary - Boosting

It is an algorithm that changes weak learners to strong learners. Boosting is a method that helps to improve the predictions of the model of the given programming language. It is not a specific but generic model. There are various models of the boosting algorithm. This algorithm shows machine learning from a different perspective. It is mostly a popular method to use to solve the problem of the weak model. Besides, boosting tracks the performance of the learner. A learner which has a better track and classification gets higher rank rather than the weaker one.


AdaBoost is a kind of algorithm that programmers developed for classification problems. It also guides poker game developers to analyze and find out data that is not classified. Then, it increases their weight. AdaBoost makes a decrease in the weights of points that are correct. It helps the classifier to focus on them and place them in the right way. 

Gradient Boosting

It is different for Gradient Boosting to approach the problem. It mainly tries to focus on the differentiation of the ground truth and prediction.

Programming specialists use these algorithms for practicing them on Python programming language.

XG Boosting Algorithm

This algorithm means Extreme Gradient Boosting. It provides the realization of the gradient boosted decision trees. These trees are for performance and speed. This machine works slowly in the process of implementation. XGBoost mainly focuses on the performance of the programming model and computational speed. It also provides:

  • Out-of-Core Computing which programmers use for a big source of information which is not possible to save in memory.

  • Distributed Computing is popular among game programmers to train larger models via the use of machines.

  • Cache Optimization. Programmers use it for structures of data and boosting algorithms to make hardware better.