PreciSplit: A Novel Approach to Predicting Polynomial Regression Data

When working with huge datasets, time complexity is crucial since it has an immediate impact on scalability and efficiency. When it comes to avoiding computing bottlenecks, accelerating model development, and preserving resources, even a small reduction in temporal complexity is crucial. The process of partitioning datasets into subsets for training, validation, and testing is commonly referred to as data splitting. This method allows hyperparameter adjustment, helps avoid bias and overfitting, and supports model selection. We may examine models’ performance on unobserved data by testing them on various test sets, which will ultimately improve generalization and system dependability. This study offers PreciSplit, a technique for precisely linear regression that uses data inflection points to intentionally partition data into almost linear segments. The approach used by PreciSplit fosters the development of trustworthy machine learning systems by conducting extensive assessments on various test sets. It shows promise in developing data analysis and enhancing model precision. With larger datasets, PreciSplit outperforms Support Vector Regression (SVR) with only a slight increase in error margin. Smaller datasets may result in poorer training speeds and accuracy.