How deeply does Statistics Slam Dunk cover area under the curve?

Defines AUC, gives benchmark values, contrasts it with classification error, and demonstrates calculation without developing its mathematical internals.

Area under the curve

As explained in Statistics Slam Dunk

AUC is the area under a receiver operating characteristic curve, measuring a binary classifier's ability to rank positive cases above negative cases.

Area under the curve (AUC) summarizes how well a binary classification model distinguishes positive cases from negative cases. It is calculated from the receiver operating characteristic (ROC) curve, which plots specificity against sensitivity. An AUC of 1 represents perfect discrimination, 0.5 represents random chance, and higher values indicate stronger overall discriminatory power. section 14.6.2

Unlike error rate and accuracy, which count individual classifications at a chosen threshold, AUC evaluates the model's overall ability to separate the two classes. Sensitivity and specificity therefore provide complementary information rather than interchangeable results. section 14.6.2

NBA playoff prediction

A logistic regression predicted whether NBA teams qualified for the playoffs. Passing actual outcomes and predictions to pROC's roc() function produced an AUC of 0.73, indicating fairly strong predictive performance. The ROC curve can then be drawn with base R's plot() function. Comparing a second model shows why AUC matters: one model can have higher sensitivity while another has higher specificity, yet their AUCs can be essentially equal. section 14.6.2

Use AUC to:

Compare binary classification models by overall discriminatory power. section 14.6.2
Interpret ROC plots alongside sensitivity, specificity, accuracy, and error rate. section 14.6.2

How Statistics Slam Dunk covers it

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Also explained in

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Grokking StatisticsThe area under the curve is the area beneath a receiver operating characteristic or precision-recall curve, summarizing performance across thresholds.
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Causal Inference for Data ScienceThe area under the ROC curve is a threshold-independent measure of binary classification performance based on the receiver operating characteristic curve.
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Machine Learning Algorithms in DepthArea under the curve is a scalar summary of the area beneath a performance curve, with larger values indicating better ranking performance in the ROC sense.

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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 Statistics Slam Dunk. The section links point to the parts of the book it draws on.