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AUC (Area Under the Curve): Difference between revisions
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{{see also|machine learning terms}} | |||
==Introduction== | ==Introduction== | ||
In [[machine learning]], the '''Area Under the ROC Curve (AUC)''' is a popular [[metric]] to assess the performance of [[binary classification]] [[models]]. This measure assesses its ability to discriminate between positive and negative [[classes]] based on [[output]] probabilities from the model. | In [[machine learning]], the '''Area Under the ROC Curve (AUC)''' is a popular [[metric]] to assess the performance of [[binary classification]] [[models]]. This measure assesses its ability to discriminate between positive and negative [[classes]] based on [[output]] probabilities from the model. | ||
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==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
AUC is like a score that tells us how well a robot is at discriminating things apart. For instance, if it has been trained to distinguish between cats and dogs, its score would be based on how many cats it can identify from all other items it examines. The higher this number is, the better equipped the robot becomes at telling cats from dogs." | AUC is like a score that tells us how well a robot is at discriminating things apart. For instance, if it has been trained to distinguish between cats and dogs, its score would be based on how many cats it can identify from all other items it examines. The higher this number is, the better equipped the robot becomes at telling cats from dogs." | ||
[[Category:Terms]] [[Category:Machine learning terms]] | |||