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Thuật ngữ AI

Từ điển đầy đủ về Trí tuệ nhân tạo

162
danh mục
2.032
danh mục con
23.060
thuật ngữ
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thuật ngữ

Expected Calibration Error (ECE)

A weighted metric calculating the average calibration error by dividing predictions into confidence bins and measuring the difference between confidence and accuracy.

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Maximum Calibration Error (MCE)

A metric identifying the worst-case disagreement between confidence and accuracy across all confidence bins, useful for assessing extreme risks.

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Reliability Diagram

A graphical visualization comparing predicted probabilities (confidence) to empirical frequencies (accuracy) to visually assess a model's calibration.

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Histogram Binning

A calibration method that discretizes prediction scores into bins and replaces each score with the empirical frequency of its corresponding class.

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Beta Calibration

A parametric technique using the Beta regression function to model the relationship between raw scores and calibrated probabilities, suited for binary predictions.

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Dirichlet Calibration

A multi-class extension of Beta calibration using the Dirichlet distribution to simultaneously calibrate all classes with interdependencies.

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Logistic Calibration

A family of parametric methods based on logistic regression to adjust predicted probabilities, including Platt Scaling as a special case.

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Class-wise Calibration

Calibration performed separately for each class in a multi-class problem, as opposed to global calibration which considers all classes simultaneously.

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Top-label Calibration

Calibration specific to the prediction with the highest probability, particularly important in systems where only the best prediction is used.

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Posterior Calibration

Process of adjusting posterior probabilities to match the true conditional distributions given the input features.

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Adaptive Calibration

Calibration methods that dynamically adapt to changing data distributions, continuously readjusting calibration parameters.

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Multi-class Calibration

Extension of binary calibration techniques to multi-class problems, requiring simultaneous calibration of probability distributions over multiple classes.

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