Abstract
Decision tree, as a decision-making method in conditions of risk and uncertainty, represents a practical graphical-visualization tool for decision-making. Additionally, modern businesses, especially banking and other financial institutions, handle large volumes of data, making an appropriate tool more essential than ever. Machine learning and associated predictive models, such as decision trees and random forest, provide a solid foundation for creating systems to make quality and timely decisions. This paper explains the background of the decision tree method and related models that are based on it. In the end, our own implementation of a system for detecting negative customer comments based on a synthetic dataset using predictive models is presented.
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Ćavar Brajković, D., Brajković, E., Volarić, T. (2024). Application of the Decision Tree in the Business Process. In: Volarić, T., Crnokić, B., Vasić, D. (eds) Digital Transformation in Education and Artificial Intelligence Application. MoStart 2024. Communications in Computer and Information Science, vol 2124. Springer, Cham. https://doi.org/10.1007/978-3-031-62058-4_17
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