Assessment of various supervised learning algorithms using different performance metrics

14th International Conference on Science, Engineering and Technology

自然科学;工业技术

Susheel Kumar, S.M.^1 ; Laxkar, Deepak^1 ; Adhikari, Sourav^1 ; Vijayarajan, V.^1

School of Computer Science and Engineering, VIT University, Vellore

632014, India^1

关键词: Binary classification;    False positive rates;    K nearest neighbours (k-NN);    Misclassification rates;    Performance metrics;    Random forests;    Supervised machine learning;    True positive rates;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042087/pdf
DOI  :  10.1088/1757-899X/263/4/042087

来源: IOP

【 摘 要 】

Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.