Calibrated Probability
A quality metric in probability theory and machine learning indicating that a model's output confidence scores closely match actual observed accuracy rates. A well-calibrated model produces predicted probabilities that can be used directly as risk probabilities for threshold-based decision making.
Timeline (last 90 days)
An honest 0.8 probability is more useful than an inflated 0.99.
Calibration means that when a model claims 80% confidence, over the long run about 80% of those are correct; an uncalibrated score is only a relative magnitude and cannot be used as a threshold.
The core capability emphasized officially by d1-ultra is calibrated probabilities, meaning the model's output probabilities should match actual occurrence frequencies.
Calibrated probability is a foundational concept in statistics and machine learning; commonly used measurement metrics include reliability diagrams and expected calibration error (ECE), and commonly used post-processing calibration methods include Platt scaling, Isotonic regression, or temperature scaling.
Every output from Jev comes with a calibrated probability; when Jev gives a probability of 0.8, it means it is correct approximately 80% of the time on that type of judgment.
Calibrated probabilities allow engineers to set thresholds for automatic escalation to human review, for example, escalating to human review when confidence falls below 60%.
Precise numerical computation is a symbolic operation process that must follow deterministic rules, which is fundamentally at odds with the token prediction mechanism based on statistical probability.
All Facts (7)
Precise numerical computation is a symbolic operation process that must follow deterministic rules, which is fundamentally at odds with the token prediction mechanism based on statistical probability.
60%UnverifiedAn honest 0.8 probability is more useful than an inflated 0.99.
50%UnverifiedCalibration means that when a model claims 80% confidence, over the long run about 80% of those are correct; an uncalibrated score is only a relative magnitude and cannot be used as a threshold.
50%UnverifiedThe core capability emphasized officially by d1-ultra is calibrated probabilities, meaning the model's output probabilities should match actual occurrence frequencies.
50%UnverifiedCalibrated probability is a foundational concept in statistics and machine learning; commonly used measurement metrics include reliability diagrams and expected calibration error (ECE), and commonly used post-processing calibration methods include Platt scaling, Isotonic regression, or temperature scaling.
50%UnverifiedEvery output from Jev comes with a calibrated probability; when Jev gives a probability of 0.8, it means it is correct approximately 80% of the time on that type of judgment.
50%UnverifiedCalibrated probabilities allow engineers to set thresholds for automatic escalation to human review, for example, escalating to human review when confidence falls below 60%.
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