Conformal Prediction

Conformal prediction is a framework for providing accuracy guarantees on the predictions of a base predictor.

[Status: No Longer Maintained | Code provided as it is]

Installation

Conformal uses the following dependencies:

• numpy,
• pyyaml
• HDF5 and h5py (optional, required if you use model saving/loading functions)

To install Conformal, cd to the conformal folder and run the install command:

python setup.py install

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Usage

cf = ConformalPrediction(model_prediction, Y_test, 5, measure=SoftMax(), threshold_mode=0)
cf_prediction = cf.predict(model_prediction)
cf_accuracy = cf.evaluate(cf_prediction, Y_test)

Please refer here for more usage details.

Contributing

1. Fork it!
2. Create your feature branch: git checkout -b my-new-feature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin my-new-feature
5. Submit a pull request :D

References:

1. http://ieeexplore.ieee.org/document/4410411/