Empirical Analysis of the AdaBoost's Error Bound

In this report, we aim to present an empirical verification A more intuitive interpretation of AdaBoost is that the algorithm of the AdaBoost algorithm (Schapire, 2013). We are going aims to combine the base classifiers by assigning to do so by first showing the theoretical error bounds along particular weights to each of them. Each weight is calculated with the necessary conditions. Afterward, we will describe in accordance with the number of misclassifications an experimental setup and report on the findings. Finally, the base classifiers return. That makes the final combined we will apply the designed experiments on both synthetic prediction of the ensemble model more robust (Opitz & and real-world data to provide empirical verification.