You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are…

Oversampling is a technique for dealing with imbalanced datasets, where the majority class

dominates the minority class. It balances the distribution of classes by increasing the number of

samples in the minority class. Oversampling can improve the performance of a classifier by reducing

the bias towards the majority class and increasing the sensitivity to the minority class.

In this case, the dataset includes transactions, of which 1% are identified as fraudulent. This means

that the fraudulent transactions are the minority class and the non-fraudulent transactions are the

majority class. A random forest model trained on this dataset might have a low recall for the

fraudulent transactions, meaning that it might miss many of them and fail to detect fraud. This could

have a high cost for the bank and its customers.

One way to overcome this problem is to oversample the fraudulent transactions 10 times, meaning

that each fraudulent transaction is duplicated 10 times in the training dataset. This would increase

the proportion of fraudulent transactions from 1% to about 10%, making the dataset more balanced.

This would also make the random forest model more aware of the patterns and features that

distinguish fraudulent transactions from non-fraudulent ones, and thus improve its accuracy and

recall for the minority class.

For more information about oversampling and other techniques for imbalanced data, see the

following

Random Oversampling and Undersampling for Imbalanced Classification

Exploring Oversampling Techniques for Imbalanced Datasets