How deeply does Machine Learning System Design cover area under the curve?

Relates values near 0.5 to indistinguishable datasets and values above 0.6 to separability.

Area under the curve

As explained in Machine Learning System Design

The area under the receiver operating characteristic curve, summarizing ranking performance across classification thresholds.

ROC AUC summarizes ranking performance across classification thresholds. In adversarial validation, a value near 0.5 suggests datasets are hard to distinguish; values above 0.6 indicate separability. section 7.3.2 section 5.2

Comparing fraud models

Two models catch 80% of fraud, but one produces far more false positives. ROC AUC favors the better model, while precision reveals why false-positive volume matters for production decisions. section 5.2

How Machine Learning System Design covers it

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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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Starting Data Analytics with Generative AI and PythonA metric equal to the area under a receiver operating characteristic curve that summarizes discrimination across thresholds.
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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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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 Machine Learning System Design. The section links point to the parts of the book it draws on.