Accuracy Optimization in Machine Learning Using Algorithm Analysis

This study investigates the relationship between model complexity, training data size, and computational cost in determining model performance. By analyzing multiple algorithms across varying scales of training data, parameter sizes, and training times, the research provides insights into how these factors influence validation accuracy. The evaluation focuses on balancing efficiency and accuracy, offering guidance for selecting appropriate configurations in machine learning experiments. Research Significance: Understanding the trade-offs between data volume, model size, and computational time is essential for optimizing machine learning pipelines. This research is significant because it identifies key performance drivers that affect accuracy, helping practitioners to allocate resources more effectively. The findings contribute to the broader field of algorithm efficiency analysis, supporting decisions in large-scale AI model development and deployment. Methodology: Algorithm Analysis: The methodology involves systematic algorithm analysis based on three primary input parameters: training data size, model parameters (in millions), and training time (in hours). A structured dataset with 150 observations was created to capture variations in these inputs and their impact on the output parameter, validation accuracy percentage. Statistical analysis and comparative evaluation were conducted to reveal performance trends and dependencies. Evaluation Parameters: The main evaluation parameter is validation accuracy percentage, which serves as the benchmark for measuring model effectiveness. This metric was selected because it provides a reliable indication of generalization capability, ensuring that the models are not only computationally efficient but also effective in real-world scenarios. Result: The analysis revealed a positive correlation between training data size and validation accuracy, though with diminishing returns beyond certain thresholds. Larger model parameter counts generally improved accuracy but at the cost of longer training times. An optimal balance was observed when moderately large datasets and parameter sizes were combined with efficient training strategies, resulting in high validation accuracy within reasonable computational budgets