Enhancing Machine Learning Model Efficiency through Quantization and Bit Depth Optimization: A Performance Analysis on Healthcare Data
This work addresses efficiency challenges for healthcare applications, but it is incremental as it applies known optimization techniques to specific datasets.
This research tackled the problem of high time complexity in machine learning models by applying quantization and bit-depth optimization to Logistic Regression on healthcare data, resulting in a significant reduction in time complexity with only a minimal decrease in accuracy.
This research aims to optimize intricate learning models by implementing quantization and bit-depth optimization techniques. The objective is to significantly cut time complexity while preserving model efficiency, thus addressing the challenge of extended execution times in intricate models. Two medical datasets were utilized as case studies to apply a Logistic Regression (LR) machine learning model. Using efficient quantization and bit depth optimization strategies the input data is downscaled from float64 to float32 and int32. The results demonstrated a significant reduction in time complexity, with only a minimal decrease in model accuracy post-optimization, showcasing the state-of-the-art optimization approach. This comprehensive study concludes that the impact of these optimization techniques varies depending on a set of parameters.