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Split conformal prediction is a distribution-free uncertainty quantification method that fits a model on a training set, computes the quantile of nonconformity scores on a calibration set as a threshold, and provides a prediction interval for a test point, guaranteeing that the probability of the true value falling within the interval is no lower than a preset confidence level (e.g., 98.4%), with this guarantee depending only on data exchangeability.
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10/19/2026
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UnverifiedSplit conformal prediction, also known as inductive conformal prediction, uses the ⌈(1-α)(n+1)⌉/n empirical quantile of the nonconformity scores as the threshold in the calibration step.77% similarUnverified分位数回归通过直接对目标变量的条件分位数建模（如同时训练 5%、50%、95% 分位点）来得到预测中位数和区间，不需要假设误差服从高斯分布，对长尾或非对称分布更稳健。75% similarUnverifiedThe fundamental issue of cross-task calibration is that the model's probability output is learned on a specific training distribution; when the input distribution changes, the reliability of the probabilities drifts but the model cannot perceive it.74% similarUnverifiedConformal prediction is an uncertainty quantification framework that outputs prediction sets or prediction intervals with rigorous statistical coverage guarantees.74% similarUnverifiedSplit conformal prediction provides non-asymptotic theoretical guarantees under the data exchangeability assumption: the prediction set covers the true label with probability at least 1-α.74% similar
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