Machine Learning Algorithms Evaluation Methods by Utilizing R

  • Hozan K. Hamarashid Information Technology Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq http://orcid.org/0000-0002-5074-7853
  • Shko M. Qader Information Technology Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq http://orcid.org/0000-0001-7520-8170
  • Soran A. Saeed Presidency of Sulaimani Polytechnic University, Higher Education and Scientific Affairs Department, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq http://orcid.org/0000-0001-8826-6716
  • Bryar A. Hassan Department of Information Technology, Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Kurdistan Region, Iraq http://orcid.org/0000-0002-4476-9351
  • Nzar A. Ali Department of Computer Science, College of Science, Cihan Univerisity of Sulaimaniya, Sulaymaniyah, Kurdistan Region, Iraq http://orcid.org/0000-0001-5309-4635
Keywords: Machine Learning algorithms, Machine Learning metric evaluation, Machine Learning test options

Abstract

Machine Learning (ML) is a part of Artificial intelligence (AI) that designs and produces systems, which is capable of developing and learning from experiences automatically without making them programmable. ML concentrates on the computer program improvement, which has the ability to access and utilize data for learning from itself. There are different algorithms in ML field, but the most important questions that arise are: Which technique should be utilized on a dataset? and How to investigate ML algorithm? This paper presents the answer for the mentioned questions. Besides, investigation and checking algorithms for a data set will be addressed. In addition, it illustrates choosing the provided test options and metrics assessment. Finally, researchers will be able to conduct this research work on their datasets to select an appropriate model for their datasets.

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Author Biographies

Hozan K. Hamarashid, Information Technology Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq

Dr Hozan K. Hamarashid is An Assistant Professor at the Department of Information Technology, Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Kurdistan Region, Iraq.

Shko M. Qader, Information Technology Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq

Shkoh M. Qader: is a lecturer at both Information Technology Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq, and  Information Technology Department, University College of Goizha, Sulaimani, Kurdistan Region, Iraq.

Soran A. Saeed, Presidency of Sulaimani Polytechnic University, Higher Education and Scientific Affairs Department, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq
Prof. Soran Prof. Dr Soran Saeed is a Vice President for Scientific Affairs and Postgraduate at Sulaimani Polytchnic University, Sulaimani, Kurdistan Region, Iraq.
Bryar A. Hassan, Department of Information Technology, Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Kurdistan Region, Iraq

Dr Bryar A. Hassan: is a PhD holder and an Assistant Professor, Kurdistan Institution for Strategic Studies and Scientific Research (KISSR), Sulaimani, Kurdistan Region, Iraq.

Nzar A. Ali, Department of Computer Science, College of Science, Cihan Univerisity of Sulaimaniya, Sulaymaniyah, Kurdistan Region, Iraq

Nzar A. Ali: is a PhD holder and An Assistant Professor at the Department of Computer Science, Cihan University of Sulaimaniya, Sulaimani, Kurdistan Region, Iraq.

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Published
2022-06-30
How to Cite
K. Hamarashid, H., Qader, S., A. Saeed, S., Hassan, B., & A. Ali, N. (2022, June 30). Machine Learning Algorithms Evaluation Methods by Utilizing R. UKH Journal of Science and Engineering, 6(1), 1-11. https://doi.org/https://doi.org/10.25079/ukhjse.v6n1y2022.pp1-11
Section
Research Articles
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