Abstract
Regression analysis is a powerful statistical method that support to inspect the relationship between two or more variables of interest. While there are many types of regression analysis, at their core they all examine the impact of one or more independent variables on a dependent variable. It is one of the most commonly used methods in many scientific fields. Satisfying the assumptions such as collinearity between variables ought to be a significant issue in data science. Advanced level tools such as Linear, Lasso, Ridge and ElasticNet regression are methods designed to overcoming a problem of overfitting a model. This study discusses comparing regression and regularization algorithms. It also deals with how the concept of model complexity unfolds for each of these models and provides an overview of how each algorithm builds a model. Moreover, it examines the strengths and weaknesses of each algorithm, as well as the type of data to which they can best be applied to irrigation water use efficiency under climate change. Finally, this work aims also to explain the meaning of the most important regularization criteria. It remains to say that the main contributions of this study are (1) Comparing linear and multilinear regression methods: case of climate change dataset using regression metrics (2) comparing regularization methods: Ridge, Lasso and ElasticNet.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aurélien G (2017) Hands-on machine learning with Scikit-Learn. O’Reilly, pp 105–139
Balducci F, Impedovo D, Informatica D, Moro A (2018) Machine learning applications on agricultural datasets for smart farm enhancement. Machines:6–38
Chang Y, Huang T, Huang N (2019) A machine learning based smart irrigation system with LoRa P2P networks. In: Asia-Pacific network operations and management symposium (APNOMS), pp 1–4
Efron B, Hastie T, Johnstone I, Tibshirani (2004) Least angle regression. Ann Stat 32:407–499
Kaplan A, Haenlein M (2018) The interpretations, illustrations, and implications of artificial intelligence. BUSHOR- 1521:11
Khairnar PP, Gaikwad DV, Kale SR, Monika T (2016) Wireless sensor network application in agriculture for monitoring agriculture production process 5(5):1302–1305
Liakos KG, Busato P, Moshou D, Pearson S (2018) Machine learning in agriculture: a review. Sensors 18(2674):1–29
Loubna H, Bouchaib N (2018) Integration of irrigation system with wireless sensor networks: prototype and conception of intelligent irrigation system. In: World congress on engineering and computer science, vol I
Majumdar J, Naraseeyappa S, Ankalaki S (2017) Analysis of agriculture data using data mining techniques: application of big data. J Big Data
Musa AB (2013) Comparative study on classification performance between support vector machine and logistic regression. Mach Learn Cyber 13–24
Ogutu JO, Schulz-streeck T, Piepho H (2012) Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proc 6(Suppl 2):S10
Oufdou H, Bellanger L, Bergam A, El Ghaziri A, Khomsi K, Qannari EM (2018) Comparison of different regularized and shrinkage regression methods to predict daily tropospheric ozone concentration in the grand Casablanca area. Adv Pure Math 793–812
Rawal S (2019) IOT based smart irrigation system IOT based smart irrigation system. Int J Comput Appl 159(8):0975–8887
Sellam V, Poovammal E (2016) Prediction of crop yield using regression analysis. Indian J Sci Technol 9(38)
Shekhar Y, Dagur E, Mishra S, Tom RJ, Veeramanikandan M (2017) Intelligent IoT based automated irrigation system. Int J Appl Eng Res 12(18):7306–7320. ISSN 0973-4562
Thevaraja M, Nagar A, Lanka S, Gabirial M (2008) Recent developments in data science: comparing linear. Ridge and Lasso regressions techniques using wine data. DISP ’19. United Kingdom, no. April, Oxford
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Raouhi, E.M., Lachgar, M., Kartit, A. (2022). Comparative Study of Regression and Regularization Methods: Application to Weather and Climate Data. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds) WITS 2020. Lecture Notes in Electrical Engineering, vol 745. Springer, Singapore. https://doi.org/10.1007/978-981-33-6893-4_22
Download citation
DOI: https://doi.org/10.1007/978-981-33-6893-4_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6892-7
Online ISBN: 978-981-33-6893-4
eBook Packages: EngineeringEngineering (R0)