The Use of Unlabeled Data in Predictive Modeling

TL;DR

This paper reviews the statistical foundations of using unlabeled data in predictive modeling, emphasizing when and why unlabeled data can improve accuracy, supported by examples and real data analyses.

Contribution

It clarifies the theoretical basis for semi-supervised learning, connecting classical sampling concepts with modern predictive modeling techniques.

Findings

Unlabeled data can enhance predictive accuracy under specific conditions.

The paper links traditional sampling theory to semi-supervised learning.

Real data examples demonstrate practical benefits of unlabeled data.

Abstract

The incorporation of unlabeled data in regression and classification analysis is an increasing focus of the applied statistics and machine learning literatures, with a number of recent examples demonstrating the potential for unlabeled data to contribute to improved predictive accuracy. The statistical basis for this semisupervised analysis does not appear to have been well delineated; as a result, the underlying theory and rationale may be underappreciated, especially by nonstatisticians. There is also room for statisticians to become more fully engaged in the vigorous research in this important area of intersection of the statistical and computer sciences. Much of the theoretical work in the literature has focused, for example, on geometric and structural properties of the unlabeled data in the context of particular algorithms, rather than probabilistic and statistical questions. This paper overviews the fundamental statistical foundations for predictive modeling and the general questions associated with unlabeled data, highlighting the relevance of venerable concepts of sampling design and prior specification. This theory, illustrated with a series of central illustrative examples and two substantial real data analyses, shows precisely when, why and how unlabeled data matter.