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Should the validation set be balanced?
2) The training fold should be kept balanced while the validation fold should be made imbalanced to reflect the original data distribution and holdout dataset.
Which type of cross validation is used for an imbalanced data set?
For example, we can use a version of k-fold cross-validation that preserves the imbalanced class distribution in each fold. It is called stratified k-fold cross-validation and will enforce the class distribution in each split of the data to match the distribution in the complete training dataset.
Is it possible to cross validation an imbalanced dataset?
Namely, the minority class is simply a rare event, which makes finding data that would hopefully balance the class distribution of the dataset very difficult. Because of these disadvantages, there can be caveats when trying to leverage cross-validation with an imbalanced dataset. These caveats are perhaps best illustrated with an example.
Is the distribution of classes in cross validation balanced?
The distribution in cross validation will obviously be different from the test set (That is classes in each fold during cross validation will be balanced. But that will not be the case in the test set). The other suggestions are good, but you could also try a balanced ensemble method.
Why are some datasets more imbalanced than others?
Difficulty Getting More Data — Datasets are often imbalanced because of real world factors, as with the credit card example mentioned above. Namely, the minority class is simply a rare event, which makes finding data that would hopefully balance the class distribution of the dataset very difficult.
Do you know if there is any cross validation technique?
Do you know if there is any cross-validation technique that does not only randomly shuffle the data, but also tries to “balance” the labels in the train set so it is 50%-50%? That would help to not get high accuracy scores, just because the model has higher changes to learn more points from a particular class.