The principle of boosting in supervised learning involves combining multiple weak classifiers to obtain a stronger classifier. AdaBoost has the reputation to be a perfect example of this approach. We have previously shown that AdaBoost is not truly an optimization algorithm. This paper shows that AdaBoost is an algorithm in name only, as the resulting combination of weak classifiers can be explicitly calculated using a truth table. This study is carried out by considering a problem with two classes and is illustrated by the particular case of three binary classifiers and presents results in comparison with those from the implementation of AdaBoost algorithm of the Python library scikit-learn.

翻译：监督学习中的提升（boosting）原理涉及组合多个弱分类器以获得更强的分类器。AdaBoost被誉为这一方法的完美范例。我们先前已证明AdaBoost并非真正的优化算法。本文进一步表明，AdaBoost仅是名义上的算法，因为其所产生的弱分类器组合可以通过真值表显式计算。该研究基于二分类问题展开，以三个二元分类器的特定情形为例进行说明，并与Python库scikit-learn中AdaBoost算法的实现结果进行了对比。