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This article explores decision trees as a powerful predictive modeling tool within game trees. A decision tree consists of interior nodes representing variables, with arcs leading to child nodes indicating possible values of these variables. The terminal leaves of the tree represent the predicted value of the target variable based on the spatial path traced from the root. By utilizing clustering techniques within decision trees, we can enhance data analysis and improve prediction accuracy. Discover how to effectively implement and interpret decision trees in your predictive analyses.
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