Classification Metrics: Core
Classification Metrics: Core groups the core ideas a learner needs at the 9 · evaluation, metrics & diagnostics stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Confusion matrixConfusion matrix is a practical concept within Classification Metrics: Core. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02AccuracyAccuracy is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.
03Balanced accuracyBalanced accuracy is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.
04PrecisionPrecision is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.
05Recall / sensitivityRecall / sensitivity is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.
06SpecificitySpecificity is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.
07F1 scoreF1 score is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.
08F-beta scoreF-beta score is a practical concept within Classification Metrics: Core. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.