How does AUC differ from other measures such as accuracy precision or recall?

How does AUC differ from other measures such as accuracy precision or recall?

AUC measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else. More importantly, AUC is not a function of threshold. It is an evaluation of the classifier as threshold varies over all possible values.

What is AUC ROC used for?

The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.

What’s the difference between ROC and ROC AUC?

A typical ROC curve looks like this, which shows two ROC curves for Algorithm 1 and Algorithm 2. The goal is to have a model be at the upper left corner, which is basically getting no false positives – a perfect classifier. The receiver operating characteristic area under curve (ROC AUC) is just the area under the ROC curve.

Which is better precision recall curve or ROC AUC?

The receiver operating characteristic area under curve (ROC AUC) is just the area under the ROC curve. The higher it is, the better the model is. Precision Recall Curve (PR Curve) A PR curve is plotting Precision against Recall. Precision is defined as: Recall is defined as:

What’s the difference between precision and precision AUC?

The goal is to have a model be at the upper right corner, which is basically getting only the true positives with no false positives and no false negatives – a perfect classifier. The precision recall area under curve (PR AUC) is just the area under the PR curve.

Is it better to use AUC or PR AUC?

That said, AUC is far less practical to finally select a model to be used for a specific task and it is entirely irrelevant to select the model’s operating point. It is perfectly possible for a suitable model for a given task to have terrible AUC under either curve. The precision of model A is higher than B up to about 11 % recall.