Exploration of Supervised Machine Learning Algorithms on Binary Classification

Supervised learning, in the milieu of Machine learning, is a type of arrangement in which we have known data with their predefined classes which help in designing classifiers. These classifiers are the base of learning for processing the data in future. This paper presents the empirical survey conducted on different Supervised Learning algorithms on varied datasets. The detailed analysis on which supervised method proves to be more efficient on what kind of data is carried out and discussed. The implications on the number and type of features are also discussed. The performance metrics used are the Accuracy, Precision and F1-score.