Measuring classifier performance: a coherent alternative to the area under the ROC curve¶
Hand. (2009). Measuring classifier performance: a coherent alternative to the area under the ROC curve: a coherent alternative to the area under the ROC curve. Machine Learning.
Cited by¶
1 citation across 1 artifact.
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Domain-specific¶
- Receiver Operating Characteristic
- Reasoning from AUC's clean reading — the probability that a randomly drawn positive outscores a randomly drawn negative — the analyst infers that a higher-AUC detector ranks cases better on average across the whole false-positive range, and may be preferred before an operating point is fixed provided the two curves do not cross inside the region that will actually be operated in — only a curve that lies above the other everywhere has higher sensitivity at every specificity.
This sourceHand's account of the AUC as the probability a randomly drawn positive outscores a randomly drawn negative, with the warning that the AUC can give misleading results when ROC curves cross.
Supported in partVerified against the source
- Reasoning from AUC's clean reading — the probability that a randomly drawn positive outscores a randomly drawn negative — the analyst infers that a higher-AUC detector ranks cases better on average across the whole false-positive range, and may be preferred before an operating point is fixed provided the two curves do not cross inside the region that will actually be operated in — only a curve that lies above the other everywhere has higher sensitivity at every specificity.
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