Keywords
Machine Learning, K-Nearest Neighbor, Precision, Recall, Naive Bayes.
Abstract
This paper discusses the use of machine learning algorithms in classifying the MNIST handwritten dataset. The MNIST dataset consists of 28x28 grayscale handwritten images with 10 classes from 0 to 9. The dataset was normalized by scaling the pixel values to a range between 0 and 1 by dividing each pixel value by 255. We compare and evaluate the K-nearest Neighbor and Naive Bayes algorithm based on performance metrics such as accuracy, error rate, f1-score, and precision. The K-nearest Neighbor algorithm achieved better performance in all the evaluation criteria.
Course Name
STA 6366 Data Science 1
Instructor Name
Dr Rui Xie
College
College of Sciences
STARS Citation
Yeboah, Felix, "Classification and Evaluation of Machine Learning Algorithms on the MNIST Dataset" (2025). Data Science and Data Mining. 27.
https://stars.library.ucf.edu/data-science-mining/27
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