Study on the Impact of Partition-Induced Dataset Shift on $k$-Fold Cross-Validation PROJECT TITLE :Study on the Impact of Partition-Induced Dataset Shift on $k$-Fold Cross-ValidationABSTRACT: 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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Reproducing Kernel Hilbert Space Approach for the Online Update of Radial Bases in Neuro-Adaptive Control Efficient Sparse Modeling With Automatic Feature Grouping
