ATLAOUI, Amel2026-01-132026-01-132025https://dspace.ummto.dz/handle/ummto/2952261f.;ill:30cmIn this thesis, we present the method in [13] which combines game theory and machine learning to develop a new decision tree model for binary classification, called the Nash Equilibrium Decision Tree (NE-DT). Unlike standard decision trees, NE-DT treats the splitting process as a game between two players (left and right branches), where each tries to best separate the data. The Nash equilibrium is a key concept in game theory ,is used to find the optimal split, improving accuracy and robustness. The work explains basic game theory and machine learning concepts, then details the NE-DT algorithm with clear mathematical steps. A simple example using the Iris dataset shows how the model works in practice. The results prove that NE-DT effectively separates classes while being easy to understand.enGame theoryNash equilibriumDecision treesBinary classificationMachine learningThesis