High precision and low recall results. What does it mean?

Question

I am working on a classification algorithm for brain rhythms. However, when I implemented the metrics for precision, accuracy, F1 score and recall. My results show that my algorithm has a high precision but a low recall.

I am not expert on this kind of metrics and analysis and I don't know if it makes sense to have a high precision but a low recall. What does it mean?

This is my reference and output models.

                "A": {
                    "FN": 5,
                    "FP": 0,
                    "Jaccard Index": 0.5454545454545454,
                    "TP": 6,
                    "f1-score": 0.7058823529411764,
                    "precision": 1.0,
                    "recall": 0.5454545454545454
                },
                "B": {
                    "FN": 34,
                    "FP": 5,
                    "Jaccard Index": 0.38095238095238093,
                    "TP": 24,
                    "f1-score": 0.5517241379310345,
                    "precision": 0.8275862068965517,
                    "recall": 0.41379310344827586
                },
                "C": {
                    "FN": 39,
                    "FP": 9,
                    "Jaccard Index": 0.36,
                    "TP": 27,
                    "f1-score": 0.5294117647058824,
                    "precision": 0.75,
                    "recall": 0.4090909090909091
                },
                "SNR": 28.121645860790924

Answer 1

Recall relates to your ability to detect the positive cases. Since you have low recall, you are missing many of those cases.

Precision relates to the credibility of a claim that a case is positive. Since you score high here, when the model flags a case as positive, you should believe it.

Combined, your model misses many of the positive cases. However, when it does flag a case as positive, the case is likely to be a positive case. Consider your model a skeptic: it is unlikely to believe a case to be positive, but when it does, it must have been because of overwhelming evidence that is worth believing.

If you know the story of the boy who cried wolf, your model does not cry wolf very often when there is no wolf, but it does miss instances of a wolf on the loose.