SPlit: An Optimal Method for Data Splitting
This method addresses the problem of suboptimal data splitting for machine learning practitioners, aiming to improve model generalization and reliability.
This paper introduces SPlit, an optimal method for splitting datasets into training and testing sets. It demonstrates substantial improvement in worst-case testing performance across various modeling methods compared to random splitting.
In this article we propose an optimal method referred to as SPlit for splitting a dataset into training and testing sets. SPlit is based on the method of Support Points (SP), which was initially developed for finding the optimal representative points of a continuous distribution. We adapt SP for subsampling from a dataset using a sequential nearest neighbor algorithm. We also extend SP to deal with categorical variables so that SPlit can be applied to both regression and classification problems. The implementation of SPlit on real datasets shows substantial improvement in the worst-case testing performance for several modeling methods compared to the commonly used random splitting procedure.