Question

Which type of cross validation is used for imbalanced dataset?

Answer 1

Solution:

the data sets are classified into 9 folds with which over sampling is done.

After training the classifier of the first 9 folds, it is time to experiment on the 10th fold.

Among all this, data should be arranged in a sequential manner to avoid any under or over sampling in case of cross validation.

Answer 2

heya answer is here...........


●Indeed, in many situations honest testing of model's performance requires balanced data representation. Apparently, this aspect is crucial for discriminant techniques that use distributions of groups to create a discriminant border (e.g. PLS-DA).

Some of model validation aspects we have discussed in our paper - B. Krakowska et al. entitled "The Monte Carlo validation framework for the discriminant partial least squares model extended with variable selection methods applied to authenticity studies of Viagra® based on chromatographic impurity profiles"

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