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        "author": "Eloy F\u00e9lix",
        "author_email": "eloyfelix@gmail.com",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3.6",
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        "description": "# ModifiedNB Model\n\nScikit-learn based implementation of the popular cheminformatics Laplace corrected Na\u00efve Bayes algorithm as described in:\n\n    Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases\n    Nidhi,\u2020, Meir Glick,\u2021, John W. Davies,\u2021 and, and Jeremy L. Jenkins*,\u2021\n    Journal of Chemical Information and Modeling 2006 46 (3), 1124-1133\n    DOI: 10.1021/ci060003g\n\n## Installation\n\n    pip install ModifiedNB\n\n## Usage\n\nWorks exactly like any other scikit-learn model.\n\n```python\nimport numpy as np\nX = np.random.randint(5, size=(6, 100))\ny = np.array([1, 2, 3, 4, 5, 6])\n\nfrom ModifiedNB import ModifiedNB\nclf = ModifiedNB()\nclf.fit(X, y)\nprint(clf.predict(X[2:3]))\n```\n\n\n",
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        "summary": "Laplace corrected modified na\u00efve bayes model",
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