I fit the random forest to my dataset with a binary target class. I reset the probabilistic cutoff to a much lower value rather than the default 0.5 according to the ROC curve. Then I can improve the sensitivity (recall) but meanwhile sacrificed the precision.
Just wanna confirm that the default 0.5 is not much meaningful and a practical probabilistic cutoff was often derived from ROC curve in practice. Am I on the right track on the application of random forest and other tree based models.
Yes, you are exactly right. 0.5 is just a heuristic, ROC curve and precision-recall curve give a much better idea of what the cut off should be. You can then use predict_proba, extract the probabilities and do the classification based on the cut-off you have inferred from ROC curve and. precision-recall curve
Yes, 0.5 is the standard cut-off value. It always depends on the business problem what threshold probability you should use to classify the values as 0 or 1.
e.g. - If you are building a fraud model, a person with fraudulent probability of 0.3 and above may make sense to be marked as fraud. Or if you are building some similarity matrix, then a value less than 0.7 could be taken at 0.
So, ROCR curve is helpful to identify the exact cut-off percentage for the specific business problem.
