Reliability
Uncertainty Quantification
Techniques that estimate how far a prediction could be wrong, as intervals, sets or spread across models.
7 techniques
7 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Monte Carlo Dropout | Algorithmic | Architecture/neural Networks Paradigm/probabilistic +4 | Any | Monte Carlo dropout estimates how uncertain a neural network is about each prediction. Dropout randomly switches off... | ||
| Quantile Regression | Algorithmic | Architecture/linear Models/regression Architecture/neural Networks +4 | Tabular | Quantile regression predicts chosen percentiles of an outcome, such as the 10th, 50th and 90th, rather than only its... | ||
| Conformal Prediction | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Conformal prediction wraps any trained model so that, instead of a single answer, it returns a set of plausible answers... | ||
| Deep Ensembles | Algorithmic | Architecture/neural Networks Paradigm/parametric +2 | Any | Deep ensembles estimate how uncertain a neural network's predictions are by training several copies of the same network... | ||
| Bootstrap Ensembles | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Bootstrap ensembles estimate how much a model's predictions depend on the particular sample of data it was trained on.... | ||
| Jackknife Resampling | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Jackknife resampling, also called leave-one-out resampling, shows how much a result computed from a dataset depends on... | ||
| Knowledge Boundary Probing | Algorithmic | Architecture/neural Networks/transformer/llm Requirements/black Box | Text | Knowledge boundary probing maps what a language model can and cannot reliably answer. A probe set is built from... |
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