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Pool Adjacent Violators Algorithm

Optimized algorithm for solving the isotonic regression problem by iteratively merging adjacent pairs that violate the monotonicity constraint, guaranteeing a quasi-linear time solution.

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Piecewise-Constant Function

Mathematical function that maintains a constant value over defined intervals, used in isotonic regression to simplify the calibration function while respecting monotonicity.

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Monotonic Constraint

Constraint imposed during optimization guaranteeing that the calibration function is strictly or weakly increasing, preserving the relative order of original predictions.

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Non-parametric Calibration

Calibration approach making no assumptions about the underlying data distribution or the parametric form of the transformation function, offering maximum flexibility.

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Confident Learning

Learning paradigm that automatically identifies and corrects labeling errors by using prediction confidence estimates to distinguish noisy examples from real patterns.

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Calibration Map

Matrix representation of the calibration function showing how each input probability is transformed into a calibrated output probability, visualizing local adjustments.

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Rank Consistency

Property guaranteed by isotonic regression that preserves the relative order of original predictions after calibration, essential for maintaining ranking decisions.

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Isotonic Constraint

Mathematical restriction requiring that the calibration function preserves the monotonic increasing order, fundamental for guaranteeing semantic consistency of transformed probabilities.

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V-Platt Scaling

Variant of Platt Scaling using cross-validation to avoid overfitting, particularly important when the calibration set is of limited size.

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Calibration Curve

Empirical curve plotting the relationship between predicted probabilities and actual occurrence rates, serving as a visual basis for evaluating and comparing different calibration methods.

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