Open AccessComparative Study of Machine Learning Algorithms on Binary Dataset
Author(s) -
Rajat Puri,
Digvijay Patil
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-887
Subject(s) - machine learning , artificial intelligence , computer science , decision tree , naive bayes classifier , computation , artificial neural network , binary classification , algorithm , support vector machine
In the world of Machine Learning, there are a lot of machine learning models to choose from for classification and decision making. Choosing the right model requires one to take in consideration various metrics like accuracy, computation time, F1 score, etc. This paper aims at comparing the performance of various such machine learning models. We use the diabetes symptoms dataset for this study. This dataset contains sixteen factors that have been seen in diabetic patients that includes age, gender, obesity, etc. The emphasis is on comparing various Machine Learning models including likes of Decision Trees, Neural Networks, etc. Decision Trees gave the best results with an accuracy of 96% and a computation time of 0.0288 seconds. Gaussian Naive Bayes was the least accurate with an accuracy of 89% and a computation time of 0.39 seconds. The great performance of Decision Trees can be attributed to the fact that the independent factors and output classes are binary and hence classification is easier and more accurate for decision trees. This paper aims at highlighting the difference in performance of various Machine Learning models based on the type of dataset used. Each model has a dataset that is most suited to it for the best possible performance.