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前言\n《Web安全深度学习实战》的读书笔记，源码和数据均来自兜哥的[Git](https://github.com/duoergun0729/2book)，代码可能根据报错进行微调，然后加了一些注释。\n\n运行环境:\n\n    MAC 10.13.3 \n    Python 3.6.4 Anaconda\n    \n# 数据预处理\n\n[TF-IDF](http://www.ruanyifeng.com/blog/2013/03/tf-idf.html)\n\n[余弦相似性](http://www.ruanyifeng.com/blog/2013/03/cosine_similarity.html)\n\n[TF-IDF、词袋模型与特征工程](https://segmentfault.com/a/1190000011480420)\n+ 词袋模型:如果一个单词在文档中出现不止一次，就统计其出现的次数，词袋在词集的基础上加入了频率这个维度，使统计拥有更好的效果，通常我们在应用中都选用词袋模型。\n+ TF-IDF:\n    + TF意思是词频(Term Frequency):某个词在语料库中出现的次数\n    + IDF意思是逆向文件频率(Inverse Document Frequency):某个词在某一类别出现的多，在其他类别出现的少，那IDF的值就会比较大\n    + 因此TF-IDF其实就是TF＊IDF\n+ 词袋模型与TF-IDF联合使用:用词袋模型筛选出一些高热度词汇，再用tf-idf计算其权值,详情见[BoW_tfidf.py](./forReadme/BoW_tfidf.py)\n\n[N-gram提取特征](https://zhuanlan.zhihu.com/p/29555001)\n\n# 常用算法\n\n[机器学习算法比较](http://www.csuldw.com/2016/02/26/2016-02-26-choosing-a-machine-learning-classifier/)\n\n[机器学习中，模型、算法如何选择？](https://zhuanlan.zhihu.com/p/32953163)\n\n![](./forReadme//AL.jpg)\n\n\n\n|    算法名     |     简介        |  Reference                                     |\n| :------------|:--------------  | :---------------------------------------------- |\n|    决策树     |   if-then      |   [【机器学习】决策树（上）——从原理到算法实现](https://blog.csdn.net/HerosOfEarth/article/details/52347820)|\n|    SVM       |   二分类        | [支持向量机(SVM)是什么意思？](https://www.zhihu.com/question/21094489) |\n|    k-means   |   聚类          | [从零开始实现Kmeans聚类算法](https://blog.csdn.net/u013719780/article/details/78413770)       |\n|    kNN       |   找出最靠近的k个样本 | [机器学习（一）——K-近邻（KNN）算法](https://www.cnblogs.com/ybjourney/p/4702562.html)|\n|    Naive Bayes |  基于概率论的分类           | [深入理解朴素贝叶斯（Naive Bayes）](https://blog.csdn.net/li8zi8fa/article/details/76176597)|\n|    AdaBoost   |   弱分类器加权集合 | [简单易学的机器学习算法——AdaBoost](https://blog.csdn.net/google19890102/article/details/46376603)|\n|    gbdt       || [简单易学的机器学习算法——梯度提升决策树GBDT](https://blog.csdn.net/google19890102/article/details/51746402)|\n|    xgboost    || [xgboost入门与实战（原理篇）](https://blog.csdn.net/sb19931201/article/details/52557382)|\n\n\n\n# 模型评估\n[sklearn中的模型评估-构建评估函数](https://www.cnblogs.com/harvey888/p/6964741.html)\n+ sklearn中的Metric：\n    ```\n    - TP，True Positive \n    - FP，False Positive \n    - TN，True Negative \n    - FN，False Negative\n    ```\n    \n    + metrics.precision_score:精确度,P = TP / (TP + FP)\n    + metrics.recall_score:召回率, R = TP / (TP + FN)\n    + metrics.accuracy_score:准确率,A = (TP + TN) / (TP + FP + TN + FN)\n    + metrics.f1_score:F = 2*P*R/(P+R)\n    + metrics.confusion_matrix:混淆矩阵\n\n    ![](./forReadme//1.png)\n    \n# 一些安装包\n\n+ 没写的可能Anaconda已经安装好了或者我之前安装过或者可以pip一键安装\n\n+ [xgboost 安装](https://stackoverflow.com/questions/40747738/importerror-no-module-named-xgboost)\n```\ngit clone --recursive https://github.com/dmlc/xgboost\ncd xgboost\nsudo cp make/minimum.mk ./config.mk;\nsudo make -j4;\nsh build.sh\ncd python-package\npython setup.py install\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnowty%2Fdl_for_sec","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsnowty%2Fdl_for_sec","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnowty%2Fdl_for_sec/lists"}