{ "info": { "author": "Bioinformatics Laboratory, FRI UL", "author_email": "info@biolab.si", "bugtrack_url": null, "classifiers": [], "description": "Orange3 Conformal Prediction\n============================\n\nConformal Prediction is an add-on for\n`Orange3 `__ data mining software package. It\nprovides an extensive toolset for conformal prediction.\n\nInstallation\n------------\n\nTo install the add-on, run\n\n::\n\n python setup.py install\n\nTo register this add-on with Orange, but keep the code in the\ndevelopment directory (do not copy it to Python's site-packages\ndirectory), run\n\n::\n\n python setup.py develop\n\nUsage\n-----\n\nThe library in the add-on can be used in Python scripts. The add-on does\nnot provide any GUI widgets.\n\nThe example below evaluates an inductive conformal predictor at 0.1\nsignificance level on the Iris dataset (spliting it into a training and\ntesting set in ratio 2:1). The nonconformity scores used by the\nconformal predictor are based on the probabilities returned by a Naive\nBayes classifier.\n\n::\n\n import Orange\n import orangecontrib.conformal as cp\n\n tab = Orange.data.Table('iris')\n nc = cp.nonconformity.InverseProbability(Orange.classification.NaiveBayesLearner())\n ic = cp.classification.InductiveClassifier(nc)\n r = cp.evaluation.run(ic, 0.1, cp.evaluation.RandomSampler(tab, 2, 1))\n print(r.accuracy())\n\nDocumentation\n-------------\n\nPlease see doc/Orange-ConformalPrediction.pdf. 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