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On the SMOTE-NC dataset, the DT model correctly predicts 3 439 positive outcomes and 3 301 negative outcomes. The number of false positives and false negatives is 341 and 207 respectively (Figure (a)). The area under curve AUC= 0.92 (Figure (c)). On the SMOTE-ENN dataset, the DT model correctly predicts 3 071 positive outcomes and 2 335 negative outcomes. The number of false positives and false negatives is 136 and 123, respectively (Figure (b)).
Accuracy score of 0.96, precision score of 0.96, recall score of 0.96 and an F1-score of 0.96 indicate that the DT model performs very well on the SMOTE-NC dataset. The cross-validation scores for the entire SMOTE-NC dataset are stable across folds and range from 0.926 to 0.934. This very small variation suggests that there is no overfitting.
The second column of Table presents the accuracy, precision, recall, and F1-scores of the DT model on the SMOTE-ENN dataset. An accuracy score of 0.95, precision score of 0.95, recall score of 0.96, and F1-score of 0.96 indicate that the DT model performs very well on the SMOTE-ENN dataset. The cross-validation scores for the entire SMOTE-ENN dataset are stable across folds and range from 0.927 to 0.934. This small variation indicates that there is no overfitting.
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IEEE/ICACT20250136 Slide.11
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