Unverified50% confidenceFactExact time
Negative log-likelihood penalizes both incorrect predictions and overconfidence, making it especially sensitive to calibration.
1
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50%
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Long-term
Relevance
11/3/2026
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Related Claims
UnverifiedRecording negative samples where 'a recommended solution did not apply' is just as important as recording successes; otherwise the model will fall into statistical bias, mistaking high-frequency solutions for high-accuracy ones79% similarVerifiedMany neural networks exhibit an overconfidence calibration bias, where output confidence scores remain close to 1 even when the error rate is high.76% similarUnverifiedThe core property of a strictly proper scoring rule is that the expected score is maximized if and only if the forecaster reports the probability they truly believe, with common examples including log-likelihood loss and the Brier score.76% similarVerifiedIn scenarios with extreme class imbalance (such as fraud detection), looking solely at accuracy as an evaluation metric is completely meaningless.74% similarUnverifiedThe log-score (i.e., cross-entropy) penalizes extreme errors more aggressively, and tends toward negative infinity when a model predicts the wrong answer with a probability close to 1.0.74% similar
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