Area under the curve

As explained in Starting Data Analytics with Generative AI and Python

A metric equal to the area under a receiver operating characteristic curve that summarizes discrimination across thresholds.

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes

Defines AUC through the ROC curve and interprets higher scores, without detailing its calculation.

Examples

Uses keyword-based positive-review classification checked against review scores as the evaluation setting.

What you can do after

Evaluate classifier discrimination across thresholds while checking sensitivity and specificity before accepting a performance trade-off.

How it connects

Links receiver operating characteristic curves with sensitivity, specificity, true-positive rates, and false-positive rates.

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Also explained in

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  Grokking Machine LearningA scalar performance metric equal to the area under a receiver operating characteristic curve.
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  Statistics Slam DunkAUC is the area under a receiver operating characteristic curve, measuring a binary classifier's ability to rank positive cases above negative cases.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIROC AUC is the area under the receiver operating characteristic curve, measuring a binary classifier's ability to rank positive instances above negative instances.
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  Outlier Detection in PythonAUROC is the area under the receiver operating characteristic curve, measuring a scoring model's ability to rank positive cases above negative cases across classification thresholds.
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  Statistics Every Programmer NeedsThe area under the ROC curve is a measure of a binary classifier's ability to discriminate between positive and negative classes across classification thresholds.
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  Machine Learning System DesignThe area under the receiver operating characteristic curve, summarizing ranking performance across classification thresholds.
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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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  Graph Neural Networks in ActionThe area under a receiver operating characteristic curve, measuring a classifier's ability to rank positive examples above negative examples across thresholds.
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  Financial AI in PracticeThe area under a receiver operating characteristic curve, summarizing a classifier’s ability to distinguish between classes across thresholds.
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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 Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.