Benchmarking Processor Performance by Multi-Threaded Machine Learning\n Algorithms

Résumé fourni par la source

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

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.