A boosting algorithm is an ensemble technique that helps to improve model performance and accuracy by taking a group of weak learners and combining them to form a strong learner. The idea behind boosting is that predictors should learn from mistakes that have been made by previous predictors.
Boosting algorithms have two key characteristics:
- First, they undergo multiple iterations
- Second, each iteration focuses on the instances that were wrongly classified by previous iterations
When an input is misclassified by a hypothesis, its weight is altered in the next iteration so that the next hypothesis can classify it correctly. More weight will be given to those that provide better performance on the training data. This process, through multiple iterations, converts weak learners into a collection of strong learners, thereby improving the model's performance...
