Row 94764

Row ID: 94764 | Dataset Entry | Axioma AXP Content Repository

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Rather than looking at generalised benchmarks, I think its better to just try different models on your actual dataset, with the task you are actually trying to do, and then look at task-specific evals.

For binary classification, look at the receiver operating characteristic curve if classes are not imbalanced, or the precision-recall curve is classes are imbalanced.

For ranking, look at normalized discounted cumulative gain for the top K items.

FieldValue
text Rather than looking at generalised benchmarks, I think its better to just try different models on your actual dataset, with the task you are actually trying to do, and then look at task-specific evals. For binary classification, look at the receiver operating characteristic curve if classes are not imbalanced, or the precision-recall curve is classes are imbalanced. For ranking, look at normalized discounted cumulative gain for the top K items.
label r/openai
dataType comment
communityName r/OpenAI
datetime 2024-05-25
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Raw Record

{
  "text": "Rather than looking at generalised benchmarks, I think its better to just try different models on your actual dataset, with the task you are actually trying to do, and then look at task-specific evals.\n\n\nFor binary classification, look at the receiver operating characteristic curve if classes are not imbalanced, or the precision-recall curve is classes are imbalanced.\n\n\nFor ranking, look at normalized discounted cumulative gain for the top K items.",
  "label": "r/openai",
  "dataType": "comment",
  "communityName": "r/OpenAI",
  "datetime": "2024-05-25",
  "username_encoded": "Z0FBQUFBQm5Lak11V0FaNkxvNzItbnJDUjlFUDNyUFdCZ19SRjlNYm9MT1lYZ3NBdjQ3VDNvV2R5T3VVSGdmWF85SkkxWjFPRnNHX3dfTzFNRmlCR2k2cWZLc0puWHplb3hHaGhmZjN6UVREUG1NZm1SZE9ob2M9",
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}
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