{ "info": { "author": "Matteo Barbieri, Samuele Fiorini, Federico Tomasi", "author_email": "{matteo.barbieri, samuele.fiorini, federico.tomasi}@dibris.unige.it", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Operating System :: MacOS", "Operating System :: POSIX", "Operating System :: Unix", "Programming Language :: Python", "Topic :: Scientific/Engineering :: Bio-Informatics", "Topic :: Software Development" ], "description": "# palladio\nParALleL frAmework for moDel selectIOn\n\n\n### Welcome to PALLADIO.\nPALLADIO is a machine learning framework whose purpose is to provide robust and reproducible results when dealing with data where the signal to noise ratio is low; it also provides tools to determine whether the dataset being analyzed contains any signal at all.\nPALLADIO works by repeating the same experiment many times, each time resampling the training and the test set so that the outcome is reliable as it is not determined by a single partition of the dataset. Besides, using permutation tests, a measure of how much experiments produce a reliable result is provided.\nSince all experiments performed are independent, PALLADIO is designed so that it can exploit a cluster where it is available.\n\n### Dependencies\nPALLADIO is developed using Python 2.7 and inherits its main functionalities from:\n* numpy\n* scipy\n* scikit-learn\n* mpi4py\n* matplotlib\n* seaborn\n\n### Authors and Contributors\nCurrent developers: Matteo Barbieri (@matteobarbieri), Samuele Fiorini (@samuelefiorini) and Federico Tomasi (@fdtomasi).\n\n\n### Support or Contact\nHaving trouble with PALLADIO? 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