What does it mean to have high precision and low recall?
A system with high precision but low recall is just the opposite, returning very few results, but most of its predicted labels are correct when compared to the training labels. An ideal system with high precision and high recall will return many results, with all results labeled correctly.
Do we want high precision or high recall?
Recall is more important than precision when the cost of acting is low, but the opportunity cost of passing up on a candidate is high.
Where is precision more important than recall?
How to interpret high precision and low recall score?
But you have a very big set of values labeled as negative, which have influence on r e c a l l = T P T P + F N, in that way that T P stays the same like in precision, but have you a lot of F N values which leads to small value of recall. At least you should resample of the dataset to make better results.
What is low recall for unbalanced dataset?
When predicting I get a low precision (0.47) for the minority class in the validation set; recall is 0.88. I tried to use several oversampling and under-sampling methods (performed on the training set) which did not improve the precision since the validation set is unbalanced as well to reflect the real class distribution.
When to use high recall in machine learning?
It is a great classifier if you data is a set of documents, if you are looking for documents of type H, and you’re main concern is to make sure that most relevant documents are retrieved (high recall on H).
When does a high recall cause high FPS?
At another, you call no example positive and have a 0% sensitivity with a 0% FPR. When the positive class is the minority, even a relatively small FPR (which you may have because you have a high recall=sensitivity=TPR) will end up causing a high number of FPs (because there are so many negative examples).