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‎homework/.DS_Store‎

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‎homework/.ipynb_checkpoints/Day6_homework_bigdata_falsenews_v2-checkpoint.ipynb‎

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‎homework/.ipynb_checkpoints/Day7_homework_machinelearning_v2-checkpoint.ipynb‎

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‎homework/Day7_homework_machinelearning_v2.ipynb‎

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‎homework/Day7_homework_machinelearning_v2.ipynb.zip‎

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‎notebook/.ipynb_checkpoints/06.05-Naive-Bayes-checkpoint.ipynb‎

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‎notebook/06.05-Naive-Bayes.ipynb‎

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"We are intrested in finding the probability of a label given some observed features, $P(L~|~{\\rm features})$.\n",
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"\n",
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"Bayes's theorem:\n",
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"$$P(L~|~{\\rm features}) = \\frac{P({\\rm features}~|~L)P(L)}{P({\\rm features})}$$\n",
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"$$P(L~|~{\\rm features}) = \\frac{P({\\rm features}~|~L)P(L)}{P({\\rm features})}=\\frac{P({\\rm features},~L)}{P({\\rm features})}$$\n",
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"\n",
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"$$\\mbox{posterior} = \\frac{\\mbox{likelihood}\\times \\mbox{prior}}{\\mbox{evidence}} \\$$\n",
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}
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"source": [
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"- **Discrimitave models** (判别式模型): \n",
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" - 给定$x$,直接建模$P(L|x)$来预测$L$\n",
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" - Decision Tree, SVM\n",
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"估计类条件概率(Likelihood)的常用策略：先假定其具有某种确定的概率分布形式，再基于训练样本对概率分布参数估计。\n",
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"\n",
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"- **Generative models** (生成式模型): \n",
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" - 先对联合概率分布$P(L, x)$建模, 再由此获得$P(L|x)$\n",
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" - Naive Bayes\n",
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" \n",
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"估计类条件概率(Likelihood)的常用策略：先假定其具有某种确定的概率分布形式，再基于训练样本对概率分布参数估计。"
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"- **Discrimitave models** (判别式模型): \n",
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" - 给定$x$,直接建模$P(L|x)$来预测$L$\n",
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" - Decision Tree, SVM\n",
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" \n"
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns; sns.set()"
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"import seaborn as sns\n",
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"sns.set()"
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"/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.datasets.base module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
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" warnings.warn(message, FutureWarning)\n"
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" 'talk.religion.misc']"
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"'sci.space'"
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"'soc.religion.christian'"
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