Comments (1)
I have added the difference score function. As for the prediction set functions, the current ones generalize properly to multivariate regression, provided that the features are contained in the last dimension of the arrays. This should be made explicit in the documentation later on.
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Related Issues (20)
- Conformal Multi-label Classification
- [Bug]: - Conflict between alpha_calib_check and weighted CP HOT 1
- Multivariate quantile
- Hypothesis testing correction
- 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
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