The Impact of Data Preprocessing Order on LASSO and Elastic Net Capabilities
Received: 14 November 2024 | Revised: 15 December 2024 and 29 December 2024 | Accepted: 1 January 2025 | Online: 2 February 2025
Corresponding author: Khuneswari Gopal Pillay
Abstract
The Food Security Index (FSI) evaluates affordability, accessibility, utilization, and food availability. However, previous research on food security in Malaysia has primarily focused on production, neglecting a detailed analysis of economic factors. The Overnight Policy Rate (OPR), set by Bank Negara Malaysia (BNM), regulates economic activity by controlling the interest rate at which commercial banks borrow and lend overnight. This study explores the impact of data preprocessing sequences on the performance of LASSO and Elastic Net regression models in predicting Malaysia's FSI. Using macroeconomic data from 2010 to 2023, this study evaluates the effects of different sequences of outlier detection and missing data imputation. The findings reveal that the LASSO model achieves the highest accuracy and the lowest error rates with outlier detection performed after imputation. This study underscores the importance of preprocessing order in enhancing model reliability and provides insight into the economic factors that influence food security in Malaysia. The results show that OPR reduces Malaysia's FSI by 0.151 units, while inflation increases it by 0.022. The LASSO regression model offers a novel perspective on the economic factors influencing food security, providing a more comprehensive understanding of food security in Malaysia.
Keywords:
preprocessing sequences, missing data, outliers, LASSO, Elastic NetDownloads
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Copyright (c) 2025 Geneveive Yii Ven Tang, Khuneswari Gopal Pillay, Aida Binti Mustapha
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