in this casewe are taking samples without replacement from non-fraud instances and then combine them with the fraud instancesnon-fraudulent observations after random under sampling of total observations after combining them with fraudulent observations + hence nowthe event rate for new dataset after under sampling the main advantage of this technique is that it can reduce run time and improve storage but on the other sideit can discard useful information while reducing the number of training data samples random over-samplingthis technique aims to balance class distribution by increasing the number of instances in the minority class by replicating them total observations fraudulent observations non-fraudulent observations event rate in case we are replicating fraudulent observations times then fraudulent observations after replicating the minority class observations would be and then total observations in the new data after oversampling would be + hence the event rate for the new data set would be / the main advantage of this method is that there would be no loss of useful information but on the other handit has the increased chances of over-fitting because it replicates the minority class events ensemble techniques this methodology basically is used to modify existing classification algorithms to make them appropriate for imbalanced data sets in this approach we construct several two stage classifier from the original data and then aggregate their predictions random forest classifier is an example of ensemble based classifier data fresh sample of data total votes cn fresh sample of data classifiers ensemble based methodology