Study on the Impact of Partition-Induced Dataset Shift on $k$ -Fold Cross-Validation
Jose G. Moreno-Torres, José A. Sáez, Francisco Herrera · IEEE Transactions on Neural Networks and Learning Systems · 2012
Cross-validation is a very commonly employed technique used to evaluate classifier performance. However, it can potentially introduce dataset shift, a harmful factor that is often not taken into account and can result in inaccurate performance estimation. This paper analyzes the prevalence and impact of partition-induced covariate shift on different k-fold cross-validation schemes. From the experimental results obtained, we conclude that the degree of partition-induced covariate shift depends on the cross-validation scheme considered. In this way, worse schemes may harm the correctness of a single-classifier performance estimation and also increase the needed number of repetitions of cross-validation to reach a stable performance estimation.