This project builds and compares decision tree classifiers using two splitting criteria: Gini impurity and Entropy (information gain). The models are evaluated using accuracy, confusion matrix, precision, recall, F1-score, and tree visualization
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This project builds and compares decision tree classifiers using two splitting criteria: Gini impurity and Entropy (information gain). The models are evaluated using accuracy, confusion matrix, precision, recall, F1-score, and tree visualization
nestalii/decision-tree
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This project builds and compares decision tree classifiers using two splitting criteria: Gini impurity and Entropy (information gain). The models are evaluated using accuracy, confusion matrix, precision, recall, F1-score, and tree visualization
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