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| Format: | Recurso digital |
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Zenodo
2026
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| Accès en ligne: | https://doi.org/10.5281/zenodo.19604213 |
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- <p>This study examines the impact of feature scaling on the performance of various classification algorithms using the Wine dataset. Five commonly used classifiers—Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest—were evaluated under two conditions: with and without feature scaling using standardization.</p> <p>The results show that distance-based algorithms such as KNN and SVM demonstrate significant improvement in performance after feature scaling, while tree-based models like Decision Tree and Random Forest remain largely unaffected. Logistic Regression shows stable performance across both scenarios.</p> <p>The findings highlight that the effectiveness of feature scaling is dependent on the type of algorithm used, emphasizing the importance of appropriate data preprocessing in machine learning workflows.</p> <p>This work was previously published in JETIR (January 2026). This version is the author's original manuscript shared as a preprint.</p> <p>Keywords: Machine Learning, Feature Scaling, Classification Algorithms, KNN, SVM, Data Preprocessing, Wine Dataset</p>