 ## Background

There are different evaluation matrices that can help with these types of datasets. Those evaluation metrics are called precision-recall evaluation metrics.

## Precision

Precision calculates, what fraction of the transactions we predicted as fraudulent(predicted class 1) are actually fraudulent. Precision can be calculated using the following formula:

## Recall

Recall tells us, what fraction of all the transactions that are originally fraudulent are detected as fraudulent. That means when a transaction is actually fraudulent if we told the proper authority of the bank to take action. When I first read these definitions of precision and recall, it took me some time to really understand the difference. I hope you are getting it faster. If not, then don’t worry. You are not alone.

## Making Decisions From Precision And Recall

The precision and recall give a better sense of how an algorithm is actually doing, especially when we have a highly skewed dataset. If we predict 0 all the time and get 99.5% accuracy, the recall and precision both will be 0. Because there are no true positives. So, you know that classifier is not a good classifier. When the precision and recall both are high, that is an indication that the algorithm is doing very well.

## F1 Score

F1 score is the average of precision and recall. But the formula for average is different. The regular average formula does not work here. Look at the average formula:

## Conclusion

In this article, you learned how to deal with a skewed dataset. How to choose between precision and recall using an F1 score. I hope it was helpful.

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