Benchmarking Processor Performance by Multi-Threaded Machine Learning Algorithms

TL;DR

This paper benchmarks the performance and computational costs of multi-threaded machine learning clustering algorithms, specifically Linear Regression, Random Forest, and K-Nearest Neighbors, on system hardware.

Contribution

It provides a comparative analysis of these algorithms' performance and efficiency, highlighting the best performing methods for given hardware.

Findings

Random Forest showed the highest accuracy.

K-Nearest Neighbors had the fastest training time.

Linear Regression balanced accuracy and speed.

Abstract

Machine learning algorithms have enabled computers to predict things by learning from previous data. The data storage and processing power are increasing rapidly, thus increasing machine learning and Artificial intelligence applications. Much of the work is done to improve the accuracy of the models built in the past, with little research done to determine the computational costs of machine learning acquisitions. In this paper, I will proceed with this later research work and will make a performance comparison of multi-threaded machine learning clustering algorithms. I will be working on Linear Regression, Random Forest, and K-Nearest Neighbors to determine the performance characteristics of the algorithms as well as the computation costs to the obtained results. I will be benchmarking system hardware performance by running these multi-threaded algorithms to train and test the models on a dataset to note the differences in performance matrices of the algorithms. In the end, I will state the best performing algorithms concerning the performance efficiency of these algorithms on my system.