A Supervised Machine Learning Algorithms: Applications, Challenges, and Recommendations


  • Aqib Ali Key Laboratory Measurement and Control of CSE Ministry of Education, School of Automation, Southeast University, Nanjing 210002, China
  • Wali Khan Mashwani Institute of Numerical Sciences, Kohat University of Science & Technology, Kohat 26000, Pakistan




Machine Learning, Supervised Learning, Classification, Supervised Algorithms


Machine Learning (ML) is an advanced technology that empowers systems to acquire knowledge autonomously, eliminating the need for explicit programming. The fundamental objective of the machine learning paradigm is to equip computers with the ability to learn independently without human intervention. In the literature, categorization in data mining has received a lot of traction, with applications ranging from health to astronomy and finance to textual classification. The three learning methodologies in machine learning are supervised, unsupervised, and semi-supervised. Humans must give the appropriate input and output and offer feedback on the prediction accuracy throughout the training phase for supervised algorithms. Unsupervised learning methods differ from supervised learning methods because they do not require any training. However, supervised learning methods are more accessible to implement than unsupervised learning methods. This study looks at supervised learning algorithms commonly employed in data classification. The strategies are evaluated based on their objective, methodology, benefits, and drawbacks.  It is anticipated that readers will be able to understand the supervised machine learning techniques for data classification.


B. Dou, Z. Zhu, E. Merkurjev, L. Ke, L. Chen, J. Jiang, and G.W. Wei. Machine learning methods for small data challenges in molecular science. Chemical Reviews 123(13): 8736-8780 (2023).

A. Ali, S. Naeem, S. Anam, and M.M. Ahmed. A Supervised Machine Learning Algorithms: Applications, Challenges, and Recommendations. International Journal of Computing and Digital Systems 13(1): 1-10 (2023).

M. Zubair. Machine Learning Based Biomedical Image Analysis and Feature Extraction Methods. Journal of Applied and Emerging Sciences 13(1): 31-39 (2023).

J.G. Greener, S. M. Kandathil, L. Moffat, and D.T. Jones. A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology 23(1): 40-55 (2022).

N. Burkart, and M.F. Huber. A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research 70: 245-317 (2021).

J. Ranjan, and C. Foropon. Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management 56: 102231 (2021).

X. Xiang and S. Foo. Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems: Part 1—Fundamentals and Applications in Games, Robotics and Natural Language Processing. Machine Learning and Knowledge Extraction 3(3): 554-581 (2021).

T. Jiang, J.L. Gradus, and A. J. Rosellini. Supervised machine learning: a brief primer. Behavior Therapy 51(5): 675-687 (2020).

A. Roohi, K. Faust, U. Djuric, and P. Diamandis. Unsupervised machine learning in pathology: the next frontier. Surgical Pathology Clinics 13(2): 349-358 (2020).

Y. Liu, S. Arunachalam, and K. Temme. A rigorous and robust quantum speed-up in supervised machine learning. Nature Physics 17(9): 1013-1017 (2021).

I.H. Sarker. Machine learning: Algorithms, real-world applications and research directions. SN Computer Science 2(3): 1-21 (2021).

S. Laghmati, B. Cherradi, A. Tmiri, O. Daanouni, and S. Hamida. Classification of patients with breast cancer using neighbourhood component analysis and supervised machine learning techniques. 3rd International Conference on Advanced Communication Technologies and Networking (CommNet) pp. 1-6, IEEE (September 2020).

L.J. Muhammad, E.A. Algehyne, and S.S. Usman. Predictive supervised machine learning models for diabetes mellitus. SN Computer Science 1(5): 1-10 (2020).

A. Rawson, and M. Brito. A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis. Transport Reviews 43(1): 108-130 (2023).

J.A. Pruneski, A. Pareek, K.N. Kunze, K.R. Martin, J. Karlsson, J.F. Oeding, and R.J. Williams III. Supervised machine learning and associated algorithms: applications in orthopedic surgery. Knee Surgery, Sports Traumatology, Arthroscopy 31(4): 1196-1202 (2023).

A. Dinesh, S. A. Selvasofia, K.S. Datcheen, and D.R. Varshan. Machine learning for strength evaluation of concrete structures–Critical review. Materials Today: Proceedings (2023).

S. Gil-Begue, C. Bielza, and P. Larrañaga. Multi-dimensional Bayesian network classifiers: A survey. Artificial Intelligence Review 54(1): 519-559 (2021).

G.A. Ruz, P.A. Henríquez, and A. Mascareño. Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Generation Computer Systems 106: 92-104 (2020).

P. Awasthi, N. Frank, and M. Mohri. On the existence of the adversarial bayes classifier. Advances in Neural Information Processing Systems 34: 2978-2990 (2021).

Z. Xue, J. Wei, and W. Guo. A real-time Naive Bayes classifier accelerator on FPGA. IEEE Access 8: 40755-40766 (2020).

K.G. Reddy, and P.S. Thilagam. Naïve Bayes classifier to mitigate the DDoS attacks severity in ad-hoc networks. International Journal of Communication Networks and Information Security 12(2): 221-226 (2020).

I.E. Tiffani. Optimization of naïve bayes classifier by implemented unigram, bigram, trigram for sentiment analysis of hotel review. Journal of Soft Computing Exploration 1(1): 1-7 (2020).

A. Kurani, P. Doshi, A. Vakharia, and M. Shah. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annals of Data Science 10(1): 183-208 (2023).

M. Tanveer, T. Rajani, R. Rastogi, Y.H. Shao, and M.A. Ganaie. Comprehensive review on twin support vector machines. Annals of Operations Research: pp. 1-46 (2022).

P. Priyanka, and D. Kumar. Decision tree classifier: A detailed survey. International Journal of Information and Decision Sciences 12(3): 246-269 (2020).

S.H. Yoo, H. Geng, T.L. Chiu, S.K. Yu, D.C. Cho, J. Heo, and H. Lee. Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging. Frontiers in Medicine 7: 427 (2020).

A. Ali, S. Naeem, S. Anam, and M.M. Ahmed. Current State of Artificial Intelligence (AI) in Oncology: A Review. Current Trends in OMICS 3(1): 01-17 (2023).

O. Mehrpour, C. Hoyte, F. Goss, F.M. Shirazi, and S. Nakhaee. Decision tree algorithm can determine the outcome of repeated supratherapeutic ingestion (RSTI) exposure to acetaminophen: review of 4500 national poison data system cases. Drug and Chemical Toxicology 46(4): 692-698 (2023).

Y.T. Chang, and N.H. Fan. A novel approach to market segmentation selection using artificial intelligence techniques. The Journal of Supercomputing 79(2): 1235-1262 (2023).

M. Farahani, S.V. Razavi-Termeh, A. Sadeghi-Niaraki, and S.M. Choi. A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes. Sustainability 15(13): 10403 (2023).

M.U. Abdulazeez, W. Khan, and K.A. Abdullah. Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset. IATSS Research 47(2): 134-159 (2023).

P. Cunningham, and S.J. Delany. k-Nearest neighbour classifiers-A Tutorial. ACM Computing Surveys (CSUR) 54(6): 1-25 (2021).

C. Li, S. Ding, X. Xu, H. Hou, and L. Ding. Fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging. Information Sciences 647: 119470 (2023).

Q. Li, S. Zhao, S. Zhao, and J. Wen. Logistic Regression Matching Pursuit algorithm for text classification. Knowledge-Based Systems 277: 110761 (2023).

G. Troiano, L. Nibali, H. Petsos, P. Eickholz, M.H. Saleh, P. Santamaria, and A. Ravidà. Development and international validation of logistic regression and machine‐learning models for the prediction of 10‐year molar loss. Journal of Clinical Periodontology 50(3): 348-357 (2023).

C. Hercus, and A.R. Hudaib. Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm. BMC Health Services Research 20(1): 1-7 (2020).




How to Cite

Aqib Ali, & Wali Khan Mashwani. (2023). A Supervised Machine Learning Algorithms: Applications, Challenges, and Recommendations. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 60(4), 1–12. https://doi.org/10.53560/PPASA(60-4)831



Review Article