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We present three evolutionary symbolic regression-based classificationalgorithms for binary and multinomial datasets: GPLearnClf, CartesianClf, andClaSyCo. Tested over 162 datasets and compared to three state-of-the-artmachine learning algorithms -- XGBoost, LightGBM, and a deep neural network --we find our algorithms to be competitive. Further, we demonstrate how to findthe best method for one's dataset automatically, through the use of astate-of-the-art hyperparameter optimizer.

Binary and Multinomial Classification through Evolutionary Symbolic Regression