{"id":17115542,"url":"https://github.com/codewithzichao/machine_learning_code","last_synced_at":"2025-05-12T14:58:11.833Z","repository":{"id":161166869,"uuid":"240990867","full_name":"codewithzichao/Machine_Learning_Code","owner":"codewithzichao","description":"《统计学习方法》与常见机器学习模型(GBDT/XGBoost/lightGBM/FM/FFM)的原理讲解与python和类库实现","archived":false,"fork":false,"pushed_at":"2020-03-18T06:00:53.000Z","size":21814,"stargazers_count":230,"open_issues_count":0,"forks_count":50,"subscribers_count":7,"default_branch":"master","last_synced_at":"2025-03-31T23:33:52.270Z","etag":null,"topics":["crf","fm-ffm","hmm","machine-learning-algorithms","python","xgboost"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/codewithzichao.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-02-17T00:43:40.000Z","updated_at":"2025-03-31T08:38:48.000Z","dependencies_parsed_at":null,"dependency_job_id":"46e43ff4-7eff-457a-87b7-617681410f17","html_url":"https://github.com/codewithzichao/Machine_Learning_Code","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codewithzichao%2FMachine_Learning_Code","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codewithzichao%2FMachine_Learning_Code/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codewithzichao%2FMachine_Learning_Code/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codewithzichao%2FMachine_Learning_Code/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/codewithzichao","download_url":"https://codeload.github.com/codewithzichao/Machine_Learning_Code/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253760816,"owners_count":21960012,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["crf","fm-ffm","hmm","machine-learning-algorithms","python","xgboost"],"created_at":"2024-10-14T17:45:05.665Z","updated_at":"2025-05-12T14:58:11.786Z","avatar_url":"https://github.com/codewithzichao.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 机器学习模型的python与类库实现\n\n本repo以李航博士的《统计学习方法》为路线，逐章讲解并实现其中所有的算法；此外，还会加上常用的机器学习模型，譬如GBDT、XGBoost、Light GBM、FM、FFM等等，力争将传统的机器学习方法能够融汇贯通🎉。\n\n## 统计学习方法｜感知机模型\n\n**模型理论讲解：**[统计学习方法｜感知机模型原理详解及实现](https://codewithzichao.github.io/2020/02/17/统计学习方法｜感知机模型原理详解与实现/#more)\n\n**模型代码实现：**[perceptron_python.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/perceptron/perceptron_python.py)、[perceptron_sklearn.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/perceptron/perceptron_sklearn.py)\n\n## 统计学习方法｜K近邻\n\n**模型理论讲解：**[统计学习方法｜K近邻算法原理详解与实现](https://codewithzichao.github.io/2020/02/26/统计学习方法｜最近邻算法原理详解与实现/#more)\n\n**模型代码实现：**[KNN_python.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/KNN/KNN_python.py)、[KNN_sklearn.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/KNN/KNN_sklearn.py)\n\n## 统计学习方法｜朴素贝叶斯\n\n**模型理论讲解：**[统计学习方法｜朴素贝叶斯模型原理详解与实现](https://codewithzichao.github.io/2020/02/18/统计学习方法-朴素贝叶斯模型详解与实现/#more)\n\n**模型代码实现：**[Naive_Bayes_python.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/Naive_Bayes/Naive_Bayes_python.py)、[Naive_Bayes_sklearn.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/Naive_Bayes/Naive_Bayes_sklearn.py)\n\n## 统计学习方法｜决策树\n\n**模型理论讲解：**[统计学习方法｜决策树模型原理详解与实现](https://codewithzichao.github.io/2020/02/27/统计学习方法-决策树模型原理详解与实现/#more)\n\n**模型代码实现：** [decision_tree_python.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/Decision_Tree/decision_tree_python.py)、[decision_tree_sklearn.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/Decision_Tree/decision_tree_sklearn.py)\n\n## 统计学习方法｜logistic回归\n\n**模型理论讲解：**[统计学习方法｜logistic回归模型详解与实现](https://codewithzichao.github.io/2020/02/20/统计学习方法-最大熵模型原理详解与实现/#more)\n\n**模型代码实现：**[logisitic_regression_python.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/MaxEnt/logistic_regression_python.py)、[logistic_regression_scikit-learn.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/MaxEnt/logistic_regression_scikit-learn.py)\n\n## 机器学习｜softmax\n\n**模型理论讲解：**[机器学习｜softmax模型原理讲解与实现](https://codewithzichao.github.io/2020/02/28/机器学习-softmax模型详解与实现/#more)\n\n**模型代码实现：**[softmax_python.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/softmax/softmax_python.py)\n\n## 统计学习方法｜最大熵模型\n\n**模型理论讲解：**[统计学习方法｜最大熵模型原理详解与实现](https://codewithzichao.github.io/2020/02/20/统计学习方法-最大熵模型原理详解与实现/#more)\n\n**模型代码实现：**[Max_Entropy.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/MaxEnt/Max_Entropy.py)\n\n## 统计学习方法｜支持向量机\n\n**模型理论讲解：**[统计学习方法｜支持向量机模型原理详解与实现](https://codewithzichao.github.io/2020/02/17/统计学习方法-支持向量机模型原理详解与实现/#more)\n\n**模型代码实现：**[SVM_python.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/SVM/SVM_python.py)、[SVM_sklearn.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/SVM/SVM_sklearn.py)\n\n## 统计学习方法｜AdaBoost\n\n**模型理论讲解：**[统计学习方法｜AdaBoost模型原理详解与实现](https://codewithzichao.github.io/2020/02/27/统计学习方法-AdaBoost模型原理详解与实现/)\n\n**模型代码实现：**\n\n## 机器学习｜XGBoost模型原理详解与实战\n\n**模型理论讲解：**[机器学习｜XGBoost模型原理详解与实战](https://codewithzichao.github.io/2020/02/28/机器学习-XGBoost模型原理详解与实战/#more)\n\n**模型应用实战**：[xgboost_mnist.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/xgboost/xgboost_mnist.py)\n\n## 机器学习｜LightGBM/catBoost\n\n**模型理论讲解：**[机器学习｜LightGBM与catBoost模型原理详解](https://codewithzichao.github.io/2020/03/04/机器学习-LightGBM与catBoost模型原理详解/#more)\n\n## 统计学习方法｜EM算法\n\n**模型理论讲解：**[统计学习方法｜EM算法与GMM模型原理详解与实现](https://codewithzichao.github.io/2020/02/22/统计学习方法-EM算法原理详解与实现/#more)\n\n**模型代码实现：**\n\n## 统计学习方法｜HMM\n\n**模型理论讲解：**[统计学习方法｜隐马尔可夫模型原理详解与实现](https://codewithzichao.github.io/2020/02/23/统计学习方法-隐马尔可夫模型原理详解与实现/#more)\n\n**模型代码实现：**[HMM_hmmlearn.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/HMM/HMM_hmmlearn.py)、[HMM_python.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/HMM/HMM_python.py)\n\n## 统计学习方法｜CRF\n\n**模型理论讲解：**[统计学习方法｜条件随机场模型原理详解与实现](https://codewithzichao.github.io/2020/02/24/统计学习方法-条件随机场模型原理详解与实现/#more)\n\n**模型代码实现：**[CRF_python.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/CRF/CRF_python.py)、[CRF_sklearn.py](https://github.com/codewithzichao/Machine_Learning_Code/blob/master/CRF/CRF_sklearn.py)\n\n## 机器学习｜FM/FFM\n\n**模型理论讲解：**[机器学习｜FM模型与FFM模型原理详解](https://codewithzichao.github.io/2020/02/28/机器学习-FM-FFM模型详解/#more)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodewithzichao%2Fmachine_learning_code","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodewithzichao%2Fmachine_learning_code","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodewithzichao%2Fmachine_learning_code/lists"}