Impact of Number of Features Selected and Size of Training Data on the Accuracy of Machine Learning Based Cloud Security Algorithms – An Empirical Analysis


  • Tanko Yahaya Mohammed Department of Computer Science Ahmadu Bello University Zaria, Nigeria.
  • Abdulrazaq Abdulrahim Department of Computer Science Ahmadu Bello University Zaria, Nigeria.
  • Muhammad Aminu Umar Dept. Computer Science, Ahmadu Bello University, Zaria, Nigeria
  • Aliyu Muhammad Kufena Department of Computer Science Ahmadu Bello University Zaria, Nigeria.
  • Hadiza Isa Abdullahi Department of Computer Science Ahmadu Bello University Zaria, Nigeria.



Empirical Analysis, Accuracy Prediction, Features Selected, Training Data


This study uses Ordinary Least Square (OLS) regression to investigate if the number of features selected and the size of training data are useful in predicting the accuracies obtained in ML based approaches to cloud security.


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