Unverified50% confidenceFactExact time
In the standard split conformal prediction pipeline, the model is first trained on a training set, then nonconformity scores are computed for each sample on an independent calibration set, and the threshold is taken as a specific quantile of these scores.
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10/9/2026
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UnverifiedThe training set is used to fit model parameters, the validation set is used to tune hyperparameters and prevent overfitting, and the test set is used for an unbiased estimate of the model's generalization ability; the three must be strictly independent.79% similarUnverifiedConformal prediction relies on two key concepts: nonconformity scores and a calibration set, where the calibration set is data with true labels that is independent of the training set.79% similarUnverifiedPerforming StandardScaler normalization on the entire dataset before the train/test split causes the mean and variance information of the test set to leak into the training set.77% similarVerifiedTeams training label-based zero-shot models should actively shuffle candidate order during evaluation as a routine robustness check.76% similarUnverifiedExamining the model's action sequences during training one by one can determine whether high scores result from genuine capability improvement or from exploiting loopholes in the reward mechanism.75% similar
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