Open AccessComparison of Lasso and stepwise regression technique for wheat yield prediction
Author(s) -
S. Prasanna Kumar,
S. D. Attri,
K. K. Singh
Publication year - 2021
Publication title -
journal of agrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 11
eISSN - 2583-2980
pISSN - 0972-1665
DOI - 10.54386/jam.v21i2.231
Subject(s) - lasso (programming language) , stepwise regression , regression analysis , statistics , regression , mathematics , linear regression , mean absolute percentage error , yield (engineering) , predictive modelling , mean squared error , feature selection , computer science , machine learning , materials science , world wide web , metallurgy
Multiple regression approach has been used to forecast the crop production widely. This study has been undertaken to evaluate the performance of stepwise and Lasso (Least absolute shrinkage and selection operator) regression technique in variable selection and development of wheat forecast model for crop yield using weather data and wheat yield for the period of 1984-2015, collected from IARI, New Delhi. Statistical parameters viz. R2, RMSE, and MAPE were 0.81, 195.90 and 4.54 per cent respectively with stepwise regression and 0.95, 99.27, 2.7 percentage, respectively with Lasso regression. Forecast models were validated during 2013-14 and 2014-15. Prediction errors were -8.5 and 10.14 per cent with stepwise and 1.89 and 1.64 percent with the Lasso. This shows that performance of Lasso regression is better than stepwise regression to some extent.