Error rate
Error rate is the proportion of evaluated operations, cases, or outcomes that produce an error or fail a specified criterion.
Sutskever's List
The proportion of predictions or classifications that are incorrect, here measured among the five highest-ranked predictions.
- How it is used
- Tracks ImageNet classification progress and frames comparisons among handcrafted-feature systems, AlexNet, later CNNs, and human annotators.
- How deep it goes
- Defines top-5 scoring through ranked guesses and examines benchmark interpretation; separately contrasts training and test error without deriving metrics mathematically.
- Examples
- Uses AlexNet’s 15.3% versus 26.2% result and a basketball ranked fourth among five guesses.
- What you can do after
- Evaluate/understand top-5 results without confusing a correct first guess with a correct top-five guess or assuming direct human comparability.
Statistics Slam Dunk
Misclassification error, or error rate, is the proportion of instances that a classification model predicts incorrectly.
- How it is used
- Evaluates incorrect binary classification predictions and supports comparisons among predictive models.
- How deep it goes
- Defines the formula, calculates it from a confusion matrix, and contrasts it with accuracy, sensitivity, and specificity.
- Examples
- Uses an NBA playoff classifier with 11 false positives, 10 false negatives, 80 records, and a 0.2625 error rate.
- What you can do after
- Apply via R by deriving error rate and accuracy from actual and predicted binary outcomes.
Statistics Every Programmer Needs
A defect rate is the number of defects divided by the number of inspected units or other relevant inspection opportunities.
- How it is used
- Evaluates incorrect classifications and monitors defect levels in processes where quality or prediction decisions require measurement.
- How deep it goes
- Defines formulas, computes rates from a confusion matrix, and distinguishes count-based from sample-size-normalized quality monitoring.
- Examples
- Uses raisin predictions with 53 false positives and 66 false negatives, plus manufacturing samples containing scratches and dents.
- What you can do after
- Evaluate classification or process quality by calculating incorrect-result rates and interpreting them with related performance measures.
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Automating API Delivery
An error rate is the proportion of API requests or operations that result in errors within a defined period.
- How it is used
- Guides continuous API monitoring, reliability diagnosis, alerting, and improvement decisions within the operation stage.
- How deep it goes
- Defines time-window failure counting, distinguishes 4xx from 5xx, and adds context, logs, thresholds, and alerts without specifying formulas.
- Examples
- Uses 401 failures by geographic region and application-log investigation of 5xx failures as diagnostic examples.
- What you can do after
- Diagnose API failures, distinguish user from server errors, and configure threshold-based alerts.
The Art of AI Product Development
Error rate is the proportion or frequency of system outputs that are incorrect within a specified set of cases.
- How it is used
- Communicates how often AI outputs fail so users calibrate attention and expectations before acting.
- How deep it goes
- Defines frequency through accuracy examples, then extends into monitoring, anomaly recognition, overrides, and reporting.
- Examples
- Uses demand forecasting, stable patterns, volatile events, misinterpreted correlations, and planner corrections.
- What you can do after
- Operate AI forecasts more safely by checking anomalies, overriding mismatches, and flagging problems for review.
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Machine Learning Algorithms in Depth
The proportion of examples whose predicted class differs from the true class.
- How it is used
- Measures classification mistakes as a loss and evaluates predictive generalization on labeled examples.
- How deep it goes
- Defines the indicator-based average and optimization goal, but does not derive an implementation or compare class-specific metrics.
- Examples
- Uses equation 1.1 and an indicator function to represent incorrect predictions across labeled examples.
- What you can do after
- Evaluate classification predictions by computing the fraction of examples whose predicted and true labels disagree.
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Related concepts
- marginmeasured by · A classification margin measures separation or confidence, expressed as distance from a decision boundary or as the correct class's score advantage over competing classes.
- reliabilitymeasures · Reliability is the ability of a system or process to perform its intended functions consistently and dependably under specified conditions and over time.
- double descentmeasured by · Double descent is a machine-learning performance pattern in which test error decreases, rises near an interpolation threshold, and then decreases again as model capacity or another resource increases.
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