Comparison of the Performance of Several Regression Algorithms in Predicting the Quality of White Wine in WEKA
Jeffrey A. Clarin
Abstract
The goal of the study was to examine the efficacy of multiple regression algorithms in predicting white wine quality. The white wine dataset from the UCI Machine Learning Repository was used. The Waikato Environment for Knowledge Analysis uses and implements regression methods. The correlation coefficient reveals that the result of performance is that the According to the results of the experiment, the Random Forest (r = 0.7459) ranked first, followed by k-Nearest Neighbor (r = 0.6225), Decision Tree (r= 0.5500), Linear regression (r =0.5257), Multi-layer perceptron (r = 0.5236), and Support Vector Machine (r= 5225). Alcohol and fixed acidity, with (r=0.48) and (r=-0.39), respectively, show a substantial correlation on regression model performance prediction, but other variables have a weak to no significant link. Future research, such as the use of additional prediction models, could be utilized to determine and compare its performance to the study's existing findings