Performance Evaluation of Extra Trees Classifier by using CPU Parallel and Non-Parallel Processing

Abstract

This research delves into assessing the performance of the Extra Trees Classifier, specifically examining the influence of CPU parallel processing on classification accuracy and computational efficiency. Fashion MNIST, a collection of grayscale images representing clothing items, serves as the foundational dataset for this study. Two variations of the Extra Trees Classifier are implemented: one configured without CPU parallel processing and another utilizing maximum CPU cores for parallel execution. The primary evaluation metrics include accuracy measurement and computational time taken for both training and prediction tasks. The findings reveal notable insights, showcasing that while the Extra Trees Classifier demonstrates commendable accuracy in classifying Fashion MNIST images, the implementation of CPU parallel processing significantly reduces computational time without compromising accuracy levels. This observation underscores the pivotal role of optimizing computational resources for efficient model training and deployment in machine learning applications. The results of this study are very helpful for understanding how to use parallel processing to make machine learning tasks more accurate and more efficient. It also shows how important it is to optimize resources for scalable and effective model development.