Boosting in data mining

What is Boosting?

Boosting is an efficient algorithm that is able to convert a weak learner into a strong learner.

Example:

Suppose we want to check that an email is “spam email” or  “safe email”?

In this case, there can be multiple possibilities like;

  • Rule 1: Email contains only links to some website.
    • Decision: It is a spam
  • Rule 2: Email from an official email address. e.g t4tutorialsfree@gmail.com.
    • Decision: It is not a spam.
  • Rule 3: Email has a request to get private bank details. e.g bank account number and father/mother name etc.
    • Decision: It is a spam
[quads id=2]

Now the question is that the 3 rules discussed above or enough to classify an email as “spam” or not?

  • Answer: These 3 rules are not enough. These 3 rules are the weak learner. So we need to boost these learners. We can boost the weak learners to the stronger learner by boosting.
  • Boosting can be done by combining and assigning weights to every weak learner.

Boosting have greater accuracy as compared to Bagging.

Types of boosting algorithm:

Three main types of boosting algorithm are as follows;

  1. XGBoost algorithm
  2. AdaBoost algorithm
  3. Gradient tree boosting algorithm
Fazal Rehman Shamil Click Here to Know More
Instructor, Researcher, Blogger, SEO Expert, Poet and Publisher of International Journal Of Software, Technology & Science ISSN : 2616 - 5325
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