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Boosting Algorithm — Machine Learning

Boosting Algorithms are a perfect match for Artificial Intelligence projects.

Boosting is a part of Machine Learning algorithms that is helpful in converting weak learns into strong ones. Basically, it is a group of meta algorithms that are essentially used in reducing bias and variance supervised learning. It also helps in reducing training errors. Due to which its application in the healthcare, IT, and Finance sectors has been fruitful and popular. Hence many AI Startups are leveraging themselves with it.

Working of Boosting Algorithm

Now we shall try to understand the working of Boosting algorithm which is very simple and easy. The main motto of the algorithm is to convert the weak learns into strong ones. For completing this task it first determines the weak link for that we need to apply a base learning algorithm with a number of different distributions. This procedure is carried out a number of times and after every procedure, we get a new weak prediction rule. Which is also known as iterative processes. And after a number of iteration boosting algorithms amalgamates all the weak rules into a single prediction. While working on this procedure major attention is given to examples that misclassified or a high large number of errors by preceding weak rules.

Types of Boosting Algorithms

Depending upon the problem underlying engines that are useful in boosting can be anything. It could be margin-maximizing classification problem, decision stamp, etc, Here we shall be looking at three types Boosting Algorithms,

  • Adaptive Boosting or Adaboost

This procedure operates iteratively, by first shortlisting misclassified data points and then adjusting their weights to reduce the training error. This is carried on continuously until it gets the strongest predictor.

  • Gradient Boosting

This method directly trains the residual error of the previous predictor.

  • Extreme Gradient Boosting or XGBoost

To help the learning to take place in parallel during the training is taking palace, it strengths multiple cores on the CPU.

Hence we can conclude that their quite a number of benefits to implementing Boosting methods like there are easy to implement, they help in reducing bias, and also have excellent computational efficiency.



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