Authors : Ayan Kumar Mahato; Cezan Mendonca; Harita Jasani; Hariharan B

Volume/Issue : Volume 10 - 2025, Issue 3 - March

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Google Scholar : https://tinyurl.com/25n2enm7

Scribd : https://tinyurl.com/yp4snu3n

DOI : https://doi.org/10.38124/ijisrt/25mar1761

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Abstract : Supervised machine learning algorithms are widely used for classification problems across various domains. However, selecting the best model requires a thorough evaluation of accuracy, robustness, and generalization ability. This research compares multiple supervised learning techniques using real- world datasets, focusing on evaluation metrics such as accuracy, sensitivity, specificity, and AUC-ROC. The study also considers the risk of overfitting, using cross- validation techniques to strengthen the conclusions. Results indicate that AdaBoost achieves near-perfect accuracy while Stochastic Gradient Descent (SGD) provides a balanced performance and generalisation, making their hybrid or combination a preferable choice for fraud detection.

Keywords : Machine Learning, Accuracy, Classification, Generalisation.

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