Machine Learning is the upcoming research area to solve various problems and classification is one of main problems in the field of machine learning. This paper describes various Supervised Machine Learning (ML) classification techniques, compares various supervised learning algorithms as well as determines the most efficient classification algorithm based on the dataset. Wine-quality-white dataset is taken from UCI machine learning repository. Six different machine learning algorithms are considered: 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). By tuning of neighbors for KNN, the best configuration is K= 1.
Thaung Myint Htun, Zaw Tun (2018); Algorithm Tuning from Comparative Analysis of Classification Algorithms; International Journal of Scientific and Research Publications (IJSRP)
8(5) (ISSN: 2250-3153), DOI: http://dx.doi.org/10.29322/IJSRP.8.5.2018.p7767