Row 42590

Row ID: 42590 | Dataset Entry | Axioma AXP Content Repository

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I think people often overlook the PR-AUC metric (in contrast to ROC-AUC). The precision-recall AUC metric measures the ability of your model to balance betwen precision and recall. A better model will have a higher score, and will allow you to work at better precision for any given recall (depending on the classification threshold you chose), or achieve a better recall for any given precision.

To the best of my knowledge, this is one of the **least** sensitive metrics to class imbalance.

The caveat is that it's mainly useful for binary classification. I don't know if a good generalization for multiclass exists.

FieldValue
text I think people often overlook the PR-AUC metric (in contrast to ROC-AUC). The precision-recall AUC metric measures the ability of your model to balance betwen precision and recall. A better model will have a higher score, and will allow you to work at better precision for any given recall (depending on the classification threshold you chose), or achieve a better recall for any given precision. To the best of my knowledge, this is one of the **least** sensitive metrics to class imbalance. T…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-22
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  "text": "I think people often overlook the PR-AUC metric (in contrast to ROC-AUC). The precision-recall AUC metric measures the ability of your model to balance betwen precision and recall. A better model will have a higher score, and will allow you to work at better precision for any given recall (depending on the classification threshold you chose), or achieve a better recall for any given precision. \n\nTo the best of my knowledge, this is one of the **least** sensitive metrics to class imbalance.\n\n  \nThe caveat is that it's mainly useful for binary classification. I don't know if a good generalization for multiclass exists.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1OZkJxWnJyaG1HVHpCRVllV1RzV1pZV0ZVSno4NFByczZaekNBWWlqbW9EbllhZHRMWDRwaWNuU0xQRWg2RW1aZF9PSHFteW1MZ3RUenRwdE1lazV0V3c9PQ==",
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}
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