Comparative Evaluation of Machine Learning Techniques for Handwritten Digit Classification Using the MNIST Dataset

Abstract

Machine learning (ML) and deep learning (DL) have advanced at a very rapid pace; thus, improving image classification in many fields. MNIST data is one of the most popular benchmark datasets to compare classification algorithms and it is represented by 70,000 grayscale images of handwritten digits (09). This paper will make a comparative analysis of a number of machine learning and deep learning algorithms and models, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) using the MNIST dataset. All of the models were trained and tested using the same preprocessing methods, including normalization and feature scaling. Several evaluation metrics were applied to evaluate the performance, and they included accuracy, precision, recall, F1-score, and confusion matrix. The findings prove that conventional machine learning models, especially SVM and Random Forest, perform competitively. Nonetheless, CNN is more effective as compared to classical models because it is capable of learning hierarchical features of space automatically using image data. The results indicate the significance of choosing suitable models depending upon the nature of data and computing limitations. This paper presents a comparative study in detail and in a systematic manner that would improve the comprehension of the trade-offs between the model performance and computational efficiency and could act as a valuable source of information to researchers and practitioners in image classification exercises.