Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms

• Published in: Data mining and knowledge discovery Vol. 38; no. 3; pp. 1364 - 1416
• Main Authors: Gomes Mantovani, Rafael, Horváth, Tomáš, Rossi, André L. D., Cerri, Ricardo, Barbon Junior, Sylvio, Vanschoren, Joaquin, Carvalho, André C. P. L. F. de
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Classification; Computer Science; Configurations; Data Mining and Knowledge Discovery; Datasets; Decision trees; Information Storage and Retrieval; Legislation; Machine learning; Optimization techniques; Performance prediction; Physics; Statistics for Engineering; Tuning; Decision tree induction algorithms; Hyperparameter profile; Hyperparameter tuning; J48; CART

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex interactions, it is common to use optimization tech...