# machine_learning_model **Repository Path**: wx_538c577e0f/machine_learning_model ## Basic Information - **Project Name**: machine_learning_model - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-05-30 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README [寻觅互联网,少有机器学习通俗易懂之算法讲解、案例等,项目立于这一问题之上,整理一份基本算法讲解+案例于文档,供大家学习之。通俗易懂之文章亦不可以面概全,但凡有不正确或争议之处,望告知,自当不吝赐教!](#) ## 目录 ## [1.线性回归](https://github.com/mantchs/machine_learning_model/tree/master/Linear%20Regression) ###     [1.1房价预测案例](https://github.com/mantchs/machine_learning_model/tree/master/Linear%20Regression/demo) ## [2.逻辑回归](https://github.com/mantchs/machine_learning_model/tree/master/Logistic%20Regression) ###     [2.1金融信用分类案例](https://github.com/mantchs/machine_learning_model/blob/master/Logistic%20Regression/demo/CreditScoring.ipynb) ## [3.决策树](https://github.com/mantchs/machine_learning_model/tree/master/Decision%20Tree) ###     [3.1决策树案例](https://github.com/mantchs/machine_learning_model/blob/master/Decision%20Tree/DecisionTree.ipynb) ###     [3.2随机森林案例](https://github.com/mantchs/machine_learning_model/blob/master/Decision%20Tree/RandomForestRegression.ipynb) ## [4.SVM](https://github.com/mantchs/machine_learning_model/tree/master/SVM) ###     [4.1新闻分类案例](https://github.com/mantchs/machine_learning_model/tree/master/SVM/cnews_demo) ## [5.模型集成(多模型融合)](https://github.com/mantchs/machine_learning_model/tree/master/Model%20Ensemble) ###     [5.1信用卡欺诈预测案例](https://github.com/mantchs/machine_learning_model/tree/master/Model%20Ensemble) ## [6.L1、L2正则化,ElasticNetCV](https://github.com/mantchs/machine_learning_model/tree/master/Regularization) ## [7.NLP从词袋到Word2Vec](https://github.com/mantchs/machine_learning_model/tree/master/Word2Vec) ###     [7.1Word2Vec训练维基百科文章](https://github.com/mantchs/machine_learning_model/blob/master/Word2Vec/word2vec.ipynb)



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