SPlit: An Optimal Method for Data Splitting

Abstract

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.

References in corpus (2)

• Information-Based Optimal Subdata Selection for Big Data Linear Regression
• Projected support points: a new method for high-dimensional data reduction

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