Supervised Learning: Classification and Comparison

Supervised Machine Learning (SML) is a search for algorithms that cause given external conditions to produce general hypotheses, and then make predictions about future events. Supervised classification is one of the most frequently performed tasks by smart systems. This paper describes various Supervised Machine Learning (ML) methods for comparing, comparing different learning algorithms and determines the best-known algorithm based on the data set, number of variables and variables (features). : Decision Table, Random Forest (RF), Naive Bayes (NB), vector Support Machine (SVM), Neural Networks (Perception), JRip and Tree Decision (J48) using learning tool the Waikato Information Machine (WEKA). In order to use algorithms, diabetes data were set up to be classified into 786 cases with eight characteristics such as independent variables and reliability analyzes. The results indicate that the SVM was found to be an algorithm with great accuracy and accuracy. Naive Bayes and Random Forest classification algorithms were found to be more accurate following SVM. Studies show that the time it takes to build a model and accuracy (accuracy) is a factor on the other hand; while statistical kappa and mean Absolute Error (MAE) are another factor on the other hand. Therefore, ML algorithms require more precision, accuracy and less error to evaluate machine learning prediction. Citation: Shweta Chaudhary. "Supervised Learning: Classification and Comparison." Global Research and Development Journal For Engineering 5.6 (2020): 8 - 14.