  Improvement of statistical learning models in order to increase efficiency in
solving classification or regression problems is still a goal pursued by the
scientific community. In this way, the support vector machine model is one of
the most successful and powerful algorithms for those tasks. However, its
performance depends directly from the choice of the kernel function and their
hyperparameters. The traditional choice of them, actually, can be
computationally expensive to do the kernel choice and the tuning processes. In
this article, it is proposed a novel framework to deal with the kernel function
selection called Random Machines. The results improved accuracy and reduced
computational time. The data study was performed in simulated data and over 27
real benchmarking datasets.
