Common CV strategies and adversarial validation

Common CV strategies:
Hold-out; Kfold, GroupKFold, StratifiedKFold, TimeSeriesSplit

Adversarial validation (adversarial verification)
concept: is a commonly used feature screening method, used to find the characteristics of obvious timing fluctuations.
operating:

1. Binary coding of the divided training set and verification set (test set), eg add an ad_target = 1 for the training set, and add an ad_target = 0 for the verification set
2. Train a simple binary classification model, the goal is to do binary classification on ad_target
3. Only one feature is used for each training, and the AUC of the validation set is recorded when the model converges
4. Iterate step 3 until all the features are traversed, and the features are sorted from high to low according to the AUC index
5. Focus on the analysis of higher AUC, the experience threshold can be selected 0.7, 0.8; pay attention to the problem of missing values

Adversarial verification (resolving CV disturbances); observing features with distribution