National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
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).
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