Receiver operating characteristic
A receiver operating characteristic curve plots a binary classifier’s true-positive rate against its false-positive rate as the decision threshold varies.
Grokking Machine Learning
A receiver operating characteristic curve is a plot of classifier sensitivity against its false-positive rate across decision thresholds.
- How it is used
- Evaluates classification models by showing how sensitivity and specificity change across decision thresholds.
- How deep it goes
- Defines sensitivity and specificity, tracks them as thresholds change, and uses AUC to compare models.
- Examples
- Uses health diagnosis and spam detection to illustrate classification thresholds, plus a worked sequence of threshold steps.
- What you can do after
- Evaluate classifier performance and select a threshold that balances sensitivity and specificity for the task.
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Statistics Slam Dunk
A ROC curve plots a binary classifier's true positive rate against its false positive rate across decision thresholds.
- How it is used
- Evaluates binary logistic-regression classifiers by comparing sensitivity, specificity, and overall discrimination.
- How deep it goes
- Defines sensitivity, specificity, AUC, and thresholded accuracy, then demonstrates computation and plotting while stopping short of threshold optimization.
- Examples
- Uses NBA playoff predictions, an 80-record confusion matrix, an AUC of 0.73, and plots comparing two logistic regressions.
- What you can do after
- Diagnose classification performance with pROC and base R, then interpret AUC alongside sensitivity, specificity, accuracy, and error rate.
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Starting Data Analytics with Generative AI and Python
A plot of true positive rate against false positive rate across classification thresholds.
- How it is used
- Compares classifier performance across decision thresholds when the acceptable sensitivity-specificity trade-off is uncertain.
- How deep it goes
- Defines the plotted rates and threshold relationship, then stops short of detailed ROC construction or mathematical derivation.
- Examples
- Uses an all-positive classifier and a sensitivity-specificity figure to illustrate threshold trade-offs.
- What you can do after
- Evaluate classifier effectiveness and make informed threshold decisions by comparing true and false positive rates.
Statistics Every Programmer Needs
A receiver operating characteristic curve plots a binary classifier's true positive rate against its false positive rate across decision thresholds.
- How it is used
- Evaluates binary-classification discrimination across thresholds and supports model optimization and deployment decisions.
- How deep it goes
- Defines ROC geometry, explains AUC and class imbalance, and demonstrates plotting with scikit-learn; it does not develop a mathematical derivation.
- Examples
- Uses raisin-variety classification, an AUC of 0.87, and a plotted random-guessing baseline.
- What you can do after
- Evaluate binary classifiers via scikit-learn's roc_auc_score() and roc_curve(), then interpret and plot their discrimination.
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Grokking Statistics
A receiver operating characteristic curve plots a classifier's true-positive rate against its false-positive rate across decision thresholds.
- How it is used
- Evaluates classifier performance across decision thresholds and exposes false-alarm versus missed-alarm tradeoffs.
- How deep it goes
- Defines TPR and FPR, explains threshold tradeoffs, and introduces AUC while deferring fuller AUC discussion.
- Examples
- Uses toddler-meltdown predictions, wolf sightings, spam email, a 25-to-100-sample expansion, and scikit-learn code.
- What you can do after
- Evaluate classifiers with scikit-learn's roc_curve and roc_auc_score across decision thresholds.
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Machine Learning Algorithms in Depth
A receiver operating characteristic curve plots the true positive rate against the false positive rate across classification thresholds.
- How it is used
- Evaluates binary-classifier performance across changing classification thresholds.
- How deep it goes
- Defines TPR and FPR, explains threshold-generated points, and summarizes quality with AUC.
- Examples
- Uses a ROC plot, the upper-left perfect-classification point, and the diagonal chance-level line.
- What you can do after
- Evaluate binary classifiers using TPR, FPR, threshold plots, and AUC.
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Financial AI in Practice
A curve that plots a classifier’s true positive rate against its false positive rate across decision thresholds.
- How it is used
- Evaluates how a credit model trades off catching defaults against falsely flagging nondefaulting borrowers across decision cutoffs.
- How deep it goes
- Defines the curve using true- and false-positive rates across cutoffs, then distinguishes it from AUC and notes its cost limitation.
- Examples
- Illustrates defaults versus nondefaults and a hypothetical rejection cutoff of 650.
- What you can do after
- Evaluate a credit model’s discrimination across cutoffs and compare ranking power before applying business rules.
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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.
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