Implementing the AdaBoost Algorithm From Scratch - KDnuggets

Boosting is an ensemble technique that attempts to create strong classifiers from a number of weak classifiers. Unlike many machine learning models which focus on high quality prediction done using single model, boosting algorithms seek to improve the prediction power by training a sequence of weak models, each compensating the weaknesses of its predecessors. Boosting grants power to machine learning models to improve their accuracy of prediction. AdaBoost, short for Adaptive Boosting, is a machine learning algorithm formulated by Yoav Freund and Robert Schapire. AdaBoost technique follows a decision tree model with a depth equal to one.