Abstract
Given a training set, Decision Trees (DTs) [Quinlan, 1986] are predictive models represented as trees where each vertex represents a feature, or attribute, and each edge represents a possible value of that attribute. Leaves contain target values and a path from the root to a leaf allows us to make a prediction. Although DTs can be used for a wide variety of tasks [Rokach and Maimon, 2014], we will focus only on classification and regression.
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Vanneschi, L., Silva, S. (2023). Decision Tree Learning. In: Lectures on Intelligent Systems. Natural Computing Series. Springer, Cham. https://doi.org/10.1007/978-3-031-17922-8_6
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DOI: https://doi.org/10.1007/978-3-031-17922-8_6
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