This study investigates the use of Recursive Feature Elimination (RFE) for feature selection in multi-output classification, particularly with a medical dataset.The classification task requires predicting several related binary outcomes simultaneously, making feature selection vital for reducing dimensionality and enhancing model performance.RFE is applied to each target variable, and common features across all targets are identified to refine the feature set while preserving prediction accuracy.A RandomForestClassifier, paired with a MultiOutputClassifier, is used to assess the predictive power of the selected features.The results indicate that RFE improves classification efficiency by removing redundant and irrelevant features, resulting in a model that is both more interpretable and computationally efficient.This method shows potential for optimizing multi-output medical classification tasks, providing advantages in healthcare data analysis.
| Jurnal | ТАТУ хабарлари |
|---|---|
| Nashr sanasi | 2024-06-01 |
| DOI | 10.61663/242tuitmct5 |
DOI: 10.61663/242tuitmct5 · Maqolaning asl sahifasi
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