Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation

Data that have not been modeled cannot be correctly predicted. Under this assumption, research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions the training and test sets to different and, therefore, a deterioration of model performance estimation. Even though stratification output variable is widely used field classification reduce impacts induced by cross-validation, its use widespread literature. paper analyzes consequences for including regressand schemes with data. The results obtained show these allow creating more similar sets, reducing presence related cross-validation. bias deviation estimation algorithms are improved using highest amounts strata, as number repetitions necessary obtain better results.