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This research paper presents the empirical analysis of machine learning and deep learning models using the Fashion Modified National Institute of Standards and Technology (MNIST) dataset from the Kaggle repository. The study found that the Visual Geometry Group (VGG) deep learning model outperformed others, achieving an F1 score of 98.06% and an accuracy of 99.40%. These results show valuable insights for selecting models for fashion object detection that may also be useful in other domains, contributing to the advancement of classification models.

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Quantifying the Effectiveness of Machine Learning and Deep Learning Models

  • 会议论文

  • 发表日期：

    2025年

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  • Kratika Jangid,
  • Mitesh Jindal,
  • Ritika Kumari,
  • Poonam Bansal

摘要

This research paper presents the empirical analysis of machine learning and deep learning models using the Fashion Modified National Institute of Standards and Technology (MNIST) dataset from the Kaggle repository. The study found that the Visual Geometry Group (VGG) deep learning model outperformed others, achieving an F1 score of 98.06% and an accuracy of 99.40%. These results show valuable insights for selecting models for fashion object detection that may also be useful in other domains, contributing to the advancement of classification models.