What question did this study set out to answer?

The research aims to compare the performance of various supervised machine learning classification algorithms on standard datasets.

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April 15, 2026Open Access

Machine Learning Using Classification: A Comparative Study of Supervised Learning Algorithms with Application to Benchmark Datasets

Key Points

The research aims to compare the performance of various supervised machine learning classification algorithms on standard datasets.
Conducted a comparative analysis of Logistic Regression, Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN).
Utilized benchmark datasets such as the Iris dataset and the Breast Cancer dataset.
Evaluated algorithms based on accuracy, precision, recall, and F1-score.
SVM achieved the highest accuracy among the tested algorithms.
Decision Tree offered better interpretability than other methods.

Abstract

This paper presents a comparative study of supervised machine learning classification algorithms including Logistic Regression, Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The study is conducted on benchmark datasets such as the Iris dataset and the Breast Cancer dataset. The performance of each algorithm is evaluated using accuracy, precision, recall, and F1-score. The results show that SVM achieves the highest accuracy, while Decision Tree provides better interpretability. This research helps beginners and MCA students understand the strengths and limitations of different classification algorithms.

Machine Learning Using Classification: A Comparative Study of Supervised Learning Algorithms with Application to Benchmark Datasets

Key Points

Abstract

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