Comments (1)
Let's go for the first fix to respect the original procedure proposed by the algorithm's authors.
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Related Issues (20)
- Multivariate quantile
- Hypothesis testing correction
- Nonconformity scores for multivariate regression HOT 1
- Update calibrator for multivariate regression.
- Update CV+ calibration for conformal multiregression
- User friendly weight normalization
- Documentation for Inductive Conformal Anomaly Detection
- [Bug]: Makefile does not run properly when no "python" is installed on machine (but only "python3", for example)
- Possible to pass sklearn model (already trained) directly to SplitCP? HOT 1
- Is there an easy way to get the quantile that you use to build the interval? HOT 1
- Unexpected behaviour: SplitCP seems to ignore my pretrained model HOT 3
- Alpha_calib_check for multivariate regression
- [Feature]: prevent empty sets (keep conformal validity, lose upper bound tight coverage)
- ConformalPredictor extension for multivariate prediction
- API: conformalizing a pretrained underlying model with no splitter
- [Bug]: Instance variable is_trained is used incorrectly HOT 2
- All MAD predictions should be positive. HOT 5
- TypeError: _quantile_dispatcher() got an unexpected keyword argument 'method' HOT 1
- [Feature Request]: - EnbPI for any regression model HOT 2
- [Feature Request]: - classification
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