Comparison of Performance of Machine Learning Algorithms for Wine Type Classification

Abstract

Supervised Machine Learning is the search for algorithms that reason from externally supplied instances to produce general hypothesis, which then make predictions about future instances. This paper describes various Supervised Machine Learning classification techniques, compares several machine learning algorithms for classifying wine types. Wine dataset is taken from UCI datasets. Six different machine learning algorithms that involve Logistic Regression(LR), Linear Discriminant Analysis(LDA), k-Nearest Neighbors(KNN), Classification and Regression Trees(CART), Gaussian Naïve Bayes (NB) and support vector machine(SVM) are proposed and assessed for this classification. The result shows that LDA was found to be the algorithm with most precision and accuracy, Gaussian Naïve Bayes and LR algorithms are found to be the next accurate after LDA accordingly. Machine Learning algorithms requires precision, accuracy and minimum error to have supervised predictive machine learning.