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

Authors(2) :-Ayoosh Kathuria, Baij Nath Kaushik

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 2 | Issue 7 | September 2017
Date of Publication : 2017-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 16-25
Manuscript Number : CSEIT174403
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

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