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Title: A machine learning approach using random forest and LASSO to predict wine quality
Authors: Athanasiadis, Ioannis
Ioannides, Dimitrios
Type: Article
Subjects: FRASCATI::Natural sciences::Mathematics::Statistics and probability
Keywords: random forest
least absolute shrinkage and selection operator
machine learning
physicochemical properties
wine quality
linear discriminant analysis
classification and regression trees
k-nearest neighbours
support vector machines
Issue Date: 30-Sep-2021
Source: International Journal of Sustainable Agricultural Management and Informatics
Volume: 7
Issue: 3
First Page: 232
Last Page: 251
Abstract: Quality assessment is a key factor for the wine industry, where the aim is to meet consumers' needs/demands and promote sales. Quality assessment is usually performed by experts and it is a time-consuming and expensive process. This paper proposes an alternative assessment using machine learning methods, such as the least absolute shrinkage and selection operator (LASSO) and random forest to predict wine quality. Our data analysis is based on a real wine dataset provided by a well-known wine firm in Greece. For this purpose, we employ the LASSO method, which is particularly effective in selecting the best possible number of variables required. Additionally, the random forest method is used and its findings are contrasted to those derived by four different M.L. methods, namely, linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbours (kNN) and support vector machines (SVM), and using the well-known ten-fold cross-validation method. The results of our analysis show that the statistical technique of random forest proposed improves the accuracy of the prediction wine quality, up to almost 95%, compared to the rankings attributed by wine tasters.
ISSN: 2054-5819
Other Identifiers: 10.1504/IJSAMI.2021.118129
Appears in Collections:Department of Economics

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