AdaBoost Algorithm Explained in Less Than 5 Minutes
The AdaBoost algorithm, which stands for Adaptive Boosting, is a boosting strategy that is applied in machine learning as part of an Ensemble Method. It is given the name “Adaptive Boosting” because of the fact that the weights are reallocated to each instance, with higher weights being applied to instances that were mistakenly categorized. Boosting is an approach to supervised learning that can reduce bias while also reducing variance.
What is AdaBoost?
AdaBoost is a supervised learning algorithm that is used for classification purposes. The algorithm is a meta-learner that is often used as a base learning algorithm to build more complex models.
AdaBoost is a boosting algorithm that creates a strong classifier by combining weak classifiers. A weak classifier is simply a classifier that performs poorly, but better than random guessing. The algorithm works by weighting the weak classifiers so that they vote with more importance.
The AdaBoost algorithm is one of the most popular boosting algorithms. It is also…
