How deeply does Financial AI in Practice cover receiver operating characteristic?

Defines the curve using true- and false-positive rates across cutoffs, then distinguishes it from AUC and notes its cost limitation.

Receiver operating characteristic

As explained in Financial AI in Practice

A curve that plots a classifier’s true positive rate against its false positive rate across decision thresholds.

In credit risk, a receiver operating characteristic (ROC) curve plots a binary classifier’s true positive rate against its false positive rate across every decision cutoff. It shows the tradeoff between catching defaulting borrowers and falsely flagging nondefaulting borrowers. The area under this curve (AUC) summarizes performance across cutoffs, but does not account for the unequal financial costs of these errors. section 4.4.1

Credit cutoff

A lender might reject applicants below a credit score of 650. The ROC curve evaluates the model across that cutoff and others, rather than judging it at 650 alone. section 4.4.1

How Financial AI in Practice covers it

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Also explained in

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Grokking Machine LearningA receiver operating characteristic curve is a plot of classifier sensitivity against its false-positive rate across decision thresholds.
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Statistics Slam DunkA ROC curve plots a binary classifier's true positive rate against its false positive rate across decision thresholds.
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Starting Data Analytics with Generative AI and PythonA plot of true positive rate against false positive rate across classification thresholds.
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Statistics Every Programmer NeedsA receiver operating characteristic curve plots a binary classifier's true positive rate against its false positive rate across decision thresholds.
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Grokking StatisticsA receiver operating characteristic curve plots a classifier's true-positive rate against its false-positive rate across decision thresholds.
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Machine Learning Algorithms in DepthA receiver operating characteristic curve plots the true positive rate against the false positive rate across classification thresholds.

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Related concepts

thresholduses · A threshold is a boundary value against which a quantity is compared to determine whether a condition is met or an action or decision is triggered.
area under the curvemeasured by · The area under a curve is the integral of its values over a specified domain and, in classification evaluation, commonly summarizes performance across decision thresholds.
recalluses · Recall is the fraction of relevant, positive, or target outcomes that a system successfully identifies, retrieves, or captures.
specificityuses · Specificity is the proportion of actual negative cases correctly identified as negative, also called the true-negative rate.
precision-recall curvealternative to · A precision-recall curve is a plot of a classifier’s precision against recall as its decision threshold varies.
logistic regressionproduced by · Logistic regression is a statistical classification model that estimates categorical outcome probabilities by applying a logistic or softmax function to a linear combination of input features.

This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.