What is the easiest way to understand boosting?
Why doesn't it boost very weak classifiers "to infinity" (perfection)?
Machine Learning – How Does Boosting Work?
boostingmachine learning
boostingmachine learning
What is the easiest way to understand boosting?
Why doesn't it boost very weak classifiers "to infinity" (perfection)?
Best Answer
In plain English: If your classifier misclassifies some data, train another copy of it mainly on this misclassified part with hope that it will discover something subtle. And then, as usual, iterate. On the way there are some voting schemes that allow to combine all those classifiers' predictions in sensible way.
Because sometimes it is impossible (the noise is just hiding some of the information, or it is not even present in the data); on the other hand, boosting too much may lead to overfitting.