Problems and Testing the Assumptions of Linear Regression: a Machine Learning Perspective

Authors

  • Ayoosh Kathuria  Shri Mata Vaishno Devi University, Kakryal, Katra, J&K, India
  • Baij Nath Kaushik  Shri Mata Vaishno Devi University, Kakryal, Katra, J&K, India

Keywords:

Time Series Prediction, Regression Analysis, Linear Regression, Machine Learning

Abstract

Linear Regression is perhaps one of most well-known algorithms in statistics and Machine Learning. Despite its widespread use in machine learning applications, the importance of testing the assumptions of linear regression is often trivialised in machine learning literature. However, the predictions of linear regressions cannot be trusted unless its assumptions are met. An attempt has been made to attract the attention of the community towards this understated aspect of putting linear regression into practice. This paper serves as an endeavour to shed some light on ways to test the assumptions of linear regressions and how to remedy the violations if there are any.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-09-30

Issue

Volume 2, Issue 7, September-2017

Section

Research Articles

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Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ayoosh Kathuria, Baij Nath Kaushik, " Problems and Testing the Assumptions of Linear Regression: a Machine Learning Perspective, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.16-25, September-2017.