What does it mean to have high recall and low precision?

What does it mean to have high recall and low precision?

The precision-recall curve shows the tradeoff between precision and recall for different threshold. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.

What is area under precision recall curve?

The area under the precision-recall curve (AUCPR) is a single number summary of the information in the precision-recall (PR) curve. Similar to the receiver operating characteristic curve, the PR curve has its own unique properties that make estimating its enclosed area challenging.

What is a precision recall curve?

A precision-recall curve shows the relationship between precision (= positive predictive value) and recall (= sensitivity) for every possible cut-off. The PRC is a graph with: • The x-axis showing recall (= sensitivity = TP / (TP + FN)) • The y-axis showing precision (= positive predictive value = TP / (TP + FP))

How is recall related to true positives and false negatives?

Recall is the number of True Positives divided by the number of True Positives and the number of False Negatives. Put another way it is the number of positive predictions divided by the number of positive class values in the test data. It is also called Sensitivity or the True Positive Rate.

Which is an example of a true negative?

True Negatives (TN) are negative outcomes that the model predicted correctly. In our example, this means that patients who were predicted to be healthy indeed does not have cancer. False Positives (FP) are positive outcomes that the model predicted incorrectly.

How is precision related to positive predictive value?

Put another way, it is the number of positive predictions divided by the total number of positive class values predicted. It is also called the Positive Predictive Value (PPV). Precision can be thought of as a measure of a classifiers exactness. A low precision can also indicate a large number of False Positives.

What are true positives and negatives in machine learning?

True positives (TP) are positive outcomes that the model predicted correctly. In our example, this means that patients who were predicted to have cancer by the model indeed does have cancer. True Negatives (TN) are negative outcomes that the model predicted correctly.