PreciSplit: A Novel Approach to Predicting Polynomial Regression Data

Abstract

Time complexity is crucial in machine learning models, especially with large datasets, as it impacts efficiency and scalability. Reducing time complexity even by a fraction is necessary to avoid computational bottlenecks and enable faster model development cycles. It saves on resources, allowing for cost-effective operations. Data splitting involves dividing datasets into subsets for training, validation, and testing. It enables unbiased evaluation, prevents overfitting, and facilitates hyperparameter tuning and model selection. By evaluating models on separate test sets, their performance on unseen data can be assessed, leading to better generalization and more reliable machine learning systems. This paper introduces PreciSplit, a highly effective approach for data splitting based on the dataset's points of inflection. By strategically dividing the data at non-linear regions, PreciSplit creates nearly linear data segments, enabling the application of precise linear regression on each split region. Additionally, the evaluation of models on distinct test sets allows for comprehensive assessment of their performance on unseen data. This evaluation process enhances generalization capabilities and fosters the devel-opment of more reliable machine learning systems. The proposed PreciSplit method showcases promising potential for advancing data analysis and model accuracy. For smaller datasets, PreciSplit shows slower training times and lower accuracy compared to Support Vector Regression (SVR).However, as the dataset size grows,it exhibits a substantial improvement in training speed, outperforming SVR, while maintaining only a slight increase in error margin.