In this ML project, I proposed a methodology that provided an outperformed performance compared to another existing paper. For the comparison here focused mainly on F1, accuracy, AUC, and ROC score. This methodology provides a 99.96% accuracy score and 90.05% F1 score.
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In this ML project, I proposed a methodology that provided an outperformed performance compared to another existing paper. For the comparison here focused mainly on F1, accuracy, AUC, and ROC score. This methodology provides a 99.96% accuracy score and 90.05% F1 score.
siam29/Hybrid-Feature-Engineering-and-Ensemble-Learning
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In this ML project, I proposed a methodology that provided an outperformed performance compared to another existing paper. For the comparison here focused mainly on F1, accuracy, AUC, and ROC score. This methodology provides a 99.96% accuracy score and 90.05% F1 score.
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