Physicochemical properties importance for type classification of wines using machine learning techniques.

Abstract

As a subfield of artificial intelligence, machine learning designed to learn the structure of the data. Machine learning has been widely used in many scientific problems. In this study, we used machine learning techniques to figure out the most important physicochemical properties for type classification of red wines. We used a wines' dataset with 13 physicochemical properties. We used a Random Forest classifier to predict wine's type from its features, and permutation feature importance, in order to detect the most important properties of the wine for type classification. The properties: flavanoids, proline, and color intensity were found to be most important for type classification. Additional 4 classifiers: Laso classifier, Ridge classifier, Decision Tree classifier, and Support Vector classifier were used and examined for classification and feature importance. Flavanoids and proline were very important across all classifiers.