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@article{183223,
        author = {Ankush Raj and Dr. Abid Sarwar and Dr. Parshotam Singh and Amit Kumar},
        title = {Performance Evaluation of Supervised Classification Algorithms on Benchmark Datasets- A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {971-978},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183223},
        abstract = {Supervised machine learning classification algorithms are widely applied across diverse domains, including healthcare, finance, and natural language processing. Selecting the most appropriate classifier for a given task remains a challenge, particularly due to variations in dataset characteristics and performance trade-offs. This study presents a comprehensive empirical evaluation of six widely used classification algorithms: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, and Random Forest [1], [3]. Each model is assessed on multiple publicly available benchmark datasets using standard performance metrics, including Accuracy, Precision, Recall, F1-Score, Area Under the Receiver Operating Characteristic Curve (AUC), and Confusion Matrix analysis. To ensure a fair comparison, all classifiers are trained under consistent experimental conditions with hyper parameter tuning applied where applicable [4], [14]. The results highlight key differences in model behavior, including strengths and limitations in terms of accuracy, interpretability, computational efficiency, and robustness to data imbalance [1], [7]. This study aims to assist researchers and practitioners in selecting suitable classification models based on empirical evidence and task-specific requirements.},
        keywords = {Supervised Machine Learning, Classification Algorithms, Hyper parameter Tuning, Performance Evaluation, Random Forest, Support Vector Machine (SVM)},
        month = {August},
        }