Performance Analysis of Supervised Learning Algorithms on Different Applications

In the current era of computation, machine learning is the most commonly used technique to find out a pattern of highly complex datasets. The present paper shows some existing applications, such as stock data mining, undergraduate admission, and breast lesion detection, where different supervised machine learning algorithms are used to classify various patterns. A performance analysis, in terms of accuracy, precision, sensitivity, and specificity is given for all three applications. It is observed that a support vector machine (SVM) is the commonly used supervised learning method that shows good performance in terms of performance metrics. A comparative analysis of SVM classifiers on the above-mentioned applications is shown in the paper.