Abstract:
"This project offers a third-party analysis of a machine learning classification task, emphasizing key evaluation metrics such as precision, recall, and F1-score to measure model performance. The dataset comprises instances divided into three distinct categories, each with varying representation levels. The developed model achieves an overall accuracy of 75%, with detailed precision and recall scores calculated for each class.
A Convolutional Neural Network (CNN) forms the core of the approach, employing convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification. The CNN architecture undergoes iterative experimentation and hyperparameter optimization to improve classification accuracy.
Performance evaluation employs precision, recall, and F1-score metrics to provide comprehensive insights into the model's effectiveness across classes. Iterative refinements in architecture and training yield notable improvements, underscoring the CNN's capability to handle challenging classification tasks. This project highlights the value of robust evaluation and model optimization from a third-party perspective, demonstrating the CNN’s potential for accurate and efficient image classification.
Keywords: Body fat risk management, biometric data, image analysis, regression models, convolutional neural networks.
Subject Descriptions:
• Health and Medicine → Body Composition → Body Fat Estimation
• Computer Science → Machine Learning → Regression Models
• Computer Vision → Convolutional Neural Networks → Image Classification
"
