Version 0.7.0

• Add prediction set estimation for binary classification.
• Add Learn-Then-Test method for multilabel-classification.
• Add documentation and notebooks for LTT.
• Add a new conformity score, ResidualNormalisedScore, that takes X into account and allows to compute adaptive intervals.
• Refactor MapieRegressor and ConformityScore to add the possibility to use X in ConformityScore.
• Separate the handling of the estimator from MapieRegressor into a new class called EnsembleEstimator.
• Add statistical calibration tests in binary classification.
• Rename methods (score to lac and cumulated_score to aps) in MapieClassifier.
• Add more notebooks and examples.
• Fix an unfixed random state in one of the classification tests.
• Fix and preserve the split behavior of the check_cv method with and without random state.

Version 0.6.5

• Add grouped conditional coverage metrics named SSC for regression and classification
• Add HSIC metric for regression
• Migrate conformity scores classes into conformity_scores module
• Migrate regression classes into regression module
• Add split conformal option for regression and classification
• Update check method for calibration
• Fix bug in MapieClassifier with different number of labels in calibration dataset.

Fix Runtime Warning with RAPS method

v0.6.4

Bump version: 0.6.3 → 0.6.4

Fix bug

Fix bug when labels are string or do not start at 0

Version 0.6.2

• Make MapieClassifier a scikit-learn object
• Update documentation for MapieClassifier

FIX issue #266 by taking into account very low scores in classification

v0.6.1

Bump version: 0.6.0 → 0.6.1

Version 0.6.0

• Add Risk-Controlling Prediction Sets (RCPS) and Conformal Risk Control (CRC)
• Add Top-Label Calibration
• Fix issues in CQR warning and cumulated scores in classification

Version 0.5.0

• Add RAPS method for classification
• Add RAPS documentation

Version 0.4.2

• Add tutorial for time series
• Convert existing tutorials in .py
• Add prefit method for CQR
• Add tutorial for CQR

Version 0.4.1

• Add packaging library in requirements
• Fix displaying problem in pypi