Rocha, AndersonPapa, João Paulo [UNESP]Meira, Luis A. A.2014-05-202014-05-202012-03-01International Journal of Pattern Recognition and Artificial Intelligence. Singapore: World Scientific Publ Co Pte Ltd, v. 26, n. 2, p. 23, 2012.0218-0014http://hdl.handle.net/11449/8295With several good research groups actively working in machine learning (ML) approaches, we have now the concept of self-containing machine learning solutions that oftentimes work out-of-the-box leading to the concept of ML black-boxes. Although it is important to have such black-boxes helping researchers to deal with several problems nowadays, it comes with an inherent problem increasingly more evident: we have observed that researchers and students are progressively relying on ML black-boxes and, usually, achieving results without knowing the machinery of the classifiers. In this regard, this paper discusses the use of machine learning black-boxes and poses the question of how far we can get using these out-of-the-box solutions instead of going deeper into the machinery of the classifiers. The paper focuses on three aspects of classifiers: (1) the way they compare examples in the feature space; (2) the impact of using features with variable dimensionality; and (3) the impact of using binary classifiers to solve a multi-class problem. We show how knowledge about the classifier's machinery can improve the results way beyond out-of-the-box machine learning solutions.23engMachine learning black-boxesbinary to multi-class classifierssupport vector machinesoptimum-path forestvisual wordsK-nearest neighborsArtigo10.1142/S0218001412610010WOS:000308104300007Acesso restrito9039182932747194