PROJECT TITLE :

Study on the Impact of Partition-Induced Dataset Shift on $k$-Fold Cross-Validation

ABSTRACT:

Cross-validation is a very commonly used technique used to judge classifier performance. But, it can doubtless introduce dataset shift, a harmful factor that's often not taken into account and can end in inaccurate performance estimation. This paper analyzes the prevalence and impact of partition-induced covariate shift on totally 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. During this way, worse schemes may hurt the correctness of one-classifier performance estimation and conjointly increase the required number of repetitions of cross-validation to reach a stable performance estimation.


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