I have a dataset with 4700 records and it's a classification problem. Proportion of classes is 33 and 67%
1) does this proportion qualify dataset as imbalanced ?
2) should I do cross validation and then apply (over/under or SMOTE sampling) or I should first balance my sample through these sampling techniques and then do cross validation?
3) Why is propensity score matching used only in healthcare related studies and not much in other applications?
4) How is Propensity score matching different from other ML classification algorithms?