International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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Original Research Articles                      Volume : 8, Issue:2, February, 2019

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : /
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2019.8(2): 1186-1191

Estimation and Comparison of Support Vector Regression with Least Square Method
S. Vishnu Shankar1*, G. Padmalakshmi2 and M. Radha3
1Agricultural Statistics
2Agricultural Economics
3Faculty of Agricultural Statistics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
*Corresponding author

Regression is one among the most used vital machine learning and statistical tool. Regression is a method of modeling a target value based on independent predictors. It allows making predictions from data by understanding the relationship between features of data and observed continuous-valued response. Support Vector Regression (SVR) is one of the useful and flexible techniques, helping the user to deal with the limitations pertaining to distributional properties of underlying variables, the geometry of the data and the common problem of model overfitting. In this paper an attempt has been made to establish the significance of SVR through the numerical study. A 34 years of Metrological data is used here to compare Support Vector Regression with Least Square Regression. Based on the numerical study SVR model is identified as best fit by using Relative Mean Square Error (RMSE).

Keywords: Least square, Support vector regression, Root mean square error

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How to cite this article:

Vishnu Shankar, S., G. Padmalakshmi and Radha, M. 2019. Estimation and Comparison of Support Vector Regression with Least Square Method.Int.J.Curr.Microbiol.App.Sci. 8(2): 1186-1191. doi:
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.