| Summary: | Artificial intelligence is given to computers' ability to imitate people's thought systems and produce solutions for complex problems. Machine learning is an important subdivision of artificial intelligence. Machine learning can be viewed as a process involving the learning of various tasks and automatic calculation methods through logical and binary inferences. R programming comes to the forefront with its success in machine learning algorithm as well as many statistical calculations. In this study, the performances of various machine learning algorithms used by R programming for classification purposes are compared. For this purpose, various machine learning algorithms have been applied to real data obtained from UCI Machine Learning Pool and classification algorithms have been compared using several criteria. The calculated criteria are; precision, accuracy, sensitivity, and classification techniques based on the F-measure. As a result of these comparisons, it is seen that Logistic Regulation algorithm, which makes the best classification in the three criteria, is more successful than the other algorithms. The algorithm that has the second best performance of all criteria has been the Navie Bayes algorithm.
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