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        "author_email": "shuo-ouyang@foxmail.com",
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        "description": "        \r\r\n        Toothpick is a mini \"library\" which contains some implementation of machine learning algorithms in Python3.6. These algorithm \r\r\n        are built on NumPy. **This \"library\" is just a toy**, the reason why I create it is to share my ideas and codes. \r\r\n        All algorithms have `fit` and `predict` interface like `scikit-learn`. When I implemented these algorithms, I referenced\r\r\n        some books as follows: _Machine Learning_ writen by ZhiHua Zhou, _Statistical Learning Method_ writen by Hang Li and \r\r\n        _Machine Learning in Action_ writen by Peter Harrington.\r\r\n        \r\r\n        By the way, I am a novice in python and machine learning field, so you can put forward issues if you find some bugs or questions.\r\r\n        \r\r\n        ## Why named toothpick?\r\r\n        \r\r\n        A toothpick is a small stick of wood, plastic, bamboo, or other substance used to remove detritus from the teeth, usually\r\r\n        after a meal(from wikipedia). This library is similarly small and you can \"take\" it after meal :P.\r\r\n        \r\r\n        ## How to guarantee the correctness?\r\r\n        \r\r\n        I compared my implementation with scikit-learn's when I was implementing these algorithms, which you can find in every single python file\r\r\n        and these performance were approximate on some very simple data set.\r\r\n        \r\r\n        ## Algorithms implemented\r\r\n        \r\r\n         - Logistic Regression\r\r\n         - Naive Bayes\r\r\n         - K Nearest Neighbours\r\r\n         - KMeans\r\r\n         - Learning Vector Quantization\r\r\n         - Ensemble Learning Algorithms\r\r\n            - AdaBoost(only support binary classification)\r\r\n            - Bagging\r\r\n            - Stacking\r\r\n        \r\r\n        ## Todo List\r\r\n        \r\r\n         - Decision Tree\r\r\n         - SVM\r\r\n         - Regression\r\r\n            - Linear Regressiong\r\r\n            - Ridge Regression\r\r\n            - Lasso Regression\r\r\n         - Neural Network\r\nPlatform: UNKNOWN\r\nClassifier: Development Status :: 3 - Alpha\r\nClassifier: Intended Audience :: Developers\r\nClassifier: License :: OSI Approved :: MIT License\r\nClassifier: Programming Language :: Python :: 3\r\nClassifier: Programming Language :: Python :: 3.4\r\nClassifier: Programming Language :: Python :: 3.5\r\nClassifier: Programming Language :: Python :: 3.6\r\nRequires-Python: ~=3.6\r\n",
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