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
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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
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