Reliability
Uncertainty Quantification
Quantifies prediction uncertainty and confidence
6 techniques in this subcategory
6 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Prediction Intervals | Algorithmic | Model Agnostic | Any | Prediction intervals provide a range of plausible values around a model's prediction, expressing uncertainty as 'the... | ||
| Quantile Regression | Algorithmic | Model Agnostic | Any | Quantile regression estimates specific percentiles (quantiles) of the target variable rather than just predicting the... | ||
| Conformal Prediction | Algorithmic | Model Agnostic | Any | Conformal prediction provides mathematically guaranteed uncertainty quantification by creating prediction sets that... | ||
| Deep Ensembles | Algorithmic | Neural Network | Any | Deep ensembles combine predictions from multiple neural networks trained independently with different random... | ||
| Jackknife Resampling | Algorithmic | Model Agnostic | Any | Jackknife resampling (also called leave-one-out resampling) assesses model stability and uncertainty by systematically... | ||
| Confidence Thresholding | Algorithmic | Model Agnostic | Any | Confidence thresholding creates decision boundaries based on model uncertainty scores, routing predictions into... |
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