What does high precision and low recall mean?
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.
What is good PR AUC?
An ideal PR-curve goes from the topleft corner horizontically to the topright corner and straight down to the bottomright corner, resulting in a PR-AUC of 1.
What makes a PR curve a good curve?
A good PR curve has greater AUC (area under curve). In the figure above, the classifier corresponding to the blue line has better performance than the classifier corresponding to the green line. It is important to note that the classifier that has a higher AUC on the ROC curve will always have a higher AUC on the PR curve as well.
Which is the recall value on a PR curve?
PR curve has the Recall value (TPR) on the x-axis, and precision = TP/ (TP+FP) on the y-axis. Precision helps highlight how relevant the retrieved results are, which is more important while judging an IR system. Hence, a PR curve is often more common around problems involving information retrieval.
How are ROC and PR curves used in machine learning?
In machine learning, when facing binary classification problems, there are two main metric tools that every data scientist uses: Receiver Operating Characteristic ( ROC) curve and Precision-Recall ( PR) curve. The main goal of this article is to cover how to interpret these curves along with their inherent confusion matrices and thresholds.
How are precision recall curves and ROC curves related?
The Precision-Recall AUC is just like the ROC AUC, in that it summarizes the curve with a range of threshold values as a single score. The score can then be used as a point of comparison between different models on a binary classification problem where a score of 1.0 represents a model with perfect skill.