# How to interpret/deal with low precision confusion matrix?

I am developing a fraud detection model with unbalanced data set , I used Random Forests Algorithm.

The confusion matrix showed that model has good sensitivity(recall) but low precision(Pos Pred Value). my question is

How to deal with low precision confusion matrix? do I need to try different Algorithm or try different tuning parameters?

There are different ways you can go about this, off the top of my head I would suggest evaluating different probability thresholds for your classification with scoring rules, as mentioned in the linked answer. Classifying an instance as positive because the associated probability estimate is simply higher than that for negative doesn't necessarily make for a good basis for decision - maybe it is best to only classify instances as positive if $$P(y=+ | X) >0.9$$ ?