Area under the curve

As explained in Grokking Machine Learning

A scalar performance metric equal to the area under a receiver operating characteristic curve.

AUC is the area beneath a receiver operating characteristic (ROC) curve, a graph that tracks a classifier’s sensitivity and specificity as its threshold changes. It compresses that curve into one score for evaluating the model. section 7.3 section 7.4

Comparing model scores

For three illustrated ROC curves, counting grid squares gives AUC values of 0.52, 0.72, and 1. A score of 1 is best; 0.5 corresponds to random guessing. A score of 0 can indicate predictions that are all reversed, which can be corrected by flipping them. section 7.3

Figure 7.5 In this figure, we can see that AUC, or area under the curve, is a good metric to determine how good a model is. The higher the AUC, the better the model. On the left, we have a bad model with an AUC of 0.52. In the middle, we have a good model with an AUC of 0.72. On the right, we have a great model with an AUC of 1.

Compare the ROC curves associated with AUC scores of 0.52, 0.72, and 1. section 7.3

How Grokking Machine Learning covers it

How deep it goes

Calculates example areas by counting grid squares and compares scores, without presenting an integral formula.

Examples

Compares model AUC values of 0.52, 0.72, and 1, and discusses reversing a model with a score of 0.

What you can do after

Evaluate classification models by comparing AUC scores and use the ROC curve to choose a threshold.

How it connects

Connects AUC to ROC curves, sensitivity, specificity, and classification thresholds.

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Related concepts

• classifiermeasures · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• receiver operating characteristicmeasures · A receiver operating characteristic curve plots a binary classifier’s true-positive rate against its false-positive rate as the decision threshold varies.
• cluster-based local outlier factormeasures · Cluster-based local outlier factor is an outlier detection algorithm that scores observations using cluster membership, cluster size, and distance to relevant clusters.
• precision-recall curvemeasures · A precision-recall curve is a plot of a classifier’s precision against recall as its decision threshold varies.
• outlier detectionmeasures · Outlier detection is the process of identifying observations that substantially deviate from an expected or prevailing pattern in data.

This explainer was generated by Manning's knowledge graph from the text of Grokking Machine Learning. The section links point to the parts of the book it draws on.