Impact of Feature Scaling on the Performance of Classification Algorithms: a Comparative Study on Wine Dataset
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2026
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| _version_ | 1866901497807110144 |
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| author | Arora, Suhani |
| author_facet | Arora, Suhani |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19604213 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Impact of Feature Scaling on the Performance of Classification Algorithms: a Comparative Study on Wine Dataset Arora, Suhani Machine Learning Feature Scaling Classification Algorithms Data Preprocessing K-Nearest Neighbors Support Vector Machine Wine Dataset <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> |
| title | Impact of Feature Scaling on the Performance of Classification Algorithms: a Comparative Study on Wine Dataset |
| topic | Machine Learning Feature Scaling Classification Algorithms Data Preprocessing K-Nearest Neighbors Support Vector Machine Wine Dataset |
| url | https://doi.org/10.5281/zenodo.19604213 |