Row 42408

Row ID: 42408 | Dataset Entry | Axioma AXP Content Repository

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area under roc curve. Even better if you have a binary classification and you are interested in detecting the positive class you could plot the precision-recall curve, and compute the average precision.

Anyway, next time please post these kinds of beginner questions on r/learnmachinelearning

Edit: missed the part on multiclass, but auroc still is good to measure

FieldValue
text area under roc curve. Even better if you have a binary classification and you are interested in detecting the positive class you could plot the precision-recall curve, and compute the average precision. Anyway, next time please post these kinds of beginner questions on r/learnmachinelearning Edit: missed the part on multiclass, but auroc still is good to measure
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-22
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{
  "text": "area under roc curve.  Even better if you have a binary classification and you are interested in detecting the positive class you could plot the precision-recall curve, and compute the average precision. \n\nAnyway, next time please post these kinds of beginner questions on r/learnmachinelearning\n\nEdit: missed the part on multiclass, but auroc still is good to measure",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1OTnlVX1I0SGlyWm1ZTU8xV2ZfSmM0T3paY2t6RlRPUFpWcU4yVmZoTFJKWkdRT1F5SHlYUGZNRUVwZDBhYXRwRXFmV0JFVTkwZEVQYzhrbnlpQTVfNjZLQTJwR1RlMk56bnNDLVNqZTV0Rnc9",
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}
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