AdaBoost Algorithm

AdaBoost, short for Adaptive Boosting, is an ensemble learning algorithm that combines many weak classifiers, each only slightly better than random guessing, into a single strong classifier by training them sequentially, increasing the weight given to training examples that previous classifiers misclassified so each new classifier focuses more on the hard cases. The final prediction is a weighted vote of all the weak classifiers, each classifier's vote weighted by its own accuracy. It was introduced by Yoav Freund and Robert Schapire in 1995, and the two later received the Godel Prize for the work.