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Log-Loss (Cross-Entropy Loss)/Evidence
Method evidence record

Log-Loss (Cross-Entropy Loss)

Log-loss measures the difference between predicted probabilities and actual labels, penalizing confident wrong predictions more than uncertain ones. It is a standard loss function in machine learning optimization and evaluates probabilistic classifier calibration.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Logarithmic Loss (Log Loss)
Taxonomic method record · mcdm / model-evaluation
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. · URL
  • Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press. · DOI 10.1093/oso/9780198538493.001.0001
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyAccuracymachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBrier Scoremachine-suggested · Relational suggestion, not evidence.Same method familyF1-Scoremachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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