What is boosting in machine learning

In the world of technology, one of the biggest inventions is Machine Learning (ML). Due to this technology which uses artificial technology or AI most of the work which needed human attention has greatly become independent.

To describe in technical terms is gathers data by using artificial technology based on algorithms and enabling the applications to collect the data and based on the data gathered it learns and can behave intuitively thus helping the application to be more precise and reliable.

There are many advantages of integrating ML or Machine Learning and in this article, we will tell you the pros and what is boosting in machine learning means.

The Pros Of Machine Learning

No Human Required

The best thing about machine learning is that it requires no human intervention to operate, once the algorithms are set then the programs or application can function on its own as this algorithm are very much advanced and doesn’t need to be altered as it independent unless the whole set up needs to be changed.

Continuous Learning

ML is a process where the learning is continuous because it gathers data and information and behaves according to the provided data and based on the repetition. For example, Virtual Personal Assitant like Siri, Alexa operates based on the behaviour of a user. If a user continuously listens to a particular song then the ML algorithm will place the song in the top of the playlist and might even play the song as the recommended song when a user requests for a song.

What is Boosting In Machine Learning?

The term boost means to magnify or add something to improve and increase the overall productivity. In Machine Learning too, the term “boosting” can be explained by the same literal meaning.

It means that the weaker algorithm is now replaced by the stronger algorithms which improve the overall productivity in the sense of reliability and accuracy.

There are steps on how you boosting is done and here it is the step.

  • First, the algorithms are set having equal weight distribution, since we are concerned with weak and strong links.
  • Then observations are done to identify the strong link and weaker link by identifying the algorithms having a higher percentage of predictions error.
  • Then repeat the above procedure and then finally we will get our strongest link that can be used in our applications.

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