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IEEE/ICACT20260118 Question.2 Questioner: 22-47918-2@student.aiub.edu 2026-02-09 오후 9:36:52	IEEE/ICACT20260118 Answer.2 Answer by Auhor sudhakar@ccet.ac.in 2026-02-09 오후 9:36:52
Your hybrid model reached 91% accuracy, which is much higher than the single models. Was this jump in performance because of the 50 selected features or the way the models were combined?	Actually, the jump in performance is a result of the synergy between the two, but the Two-Stage Architecture was the primary catalyst. Our hybrid feature selection—using Mutual Information and ANOVA—was essential for stripping away noise from the original 257 features and ensuring we werent dealing with dimensionality issues. This provided a much cleaner foundation for the models to learn from. However, as you can see from our baseline results, even with high-quality features, standalone models like Random Forest and XGBoost stayed under 80%. The significant 13% leap to 91.49% happened specifically because of the Anomaly Override. By adding that second layer with the Isolation Forest, we created a mechanism that specifically catches and corrects edge cases or statistical outliers that typical supervised classifiers struggle to generalize. So, to summarize: the feature selection made the models efficient, but the hybrid combination made the entire system resilient.

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