Benchmarking Assessment of Supervised Machine Learning Algorithms of K-Nearest Neighbor, Random Forest, Decision Tree and Its Variants Based On Efficiency and Performance Metrics

Abstract

Machine learning provides more verbose algorithms capable of accurately predicting, classifying groups as needed. Consequently, the objective of this paper is to assess the benchmarking of Supervised Machine Learning Algorithms of K-Nearest Neighbor, Random Forest, Decision Tree and it variants (ID3, C4.5, C5.0 and CART) based on efficiency and performance metrics using python programming after downloading dataset from Kaggle repository. Dataset to the aforementioned models reveals that, the C4.5 variant of decision tree had the highest prediction accuracy, CART and KNN had the minimal learning and prediction time. If accuracy is the based preference, C4.5 variant of decision tree should be recognized, but when the chief concern is nominal time for training and prediction, then CART and KNN standout.