{"id":20766174,"url":"https://github.com/navono/mlsimpletutorial","last_synced_at":"2026-04-21T15:31:36.081Z","repository":{"id":74496988,"uuid":"98603311","full_name":"navono/MLSimpleTutorial","owner":"navono","description":"Sample code from http://machinelearningmastery.com/machine-learning-in-python-step-by-step/","archived":false,"fork":false,"pushed_at":"2017-07-29T03:56:23.000Z","size":5,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-11T18:54:57.166Z","etag":null,"topics":["machine-learning","python3"],"latest_commit_sha":null,"homepage":null,"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/navono.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":"2017-07-28T03:18:13.000Z","updated_at":"2017-07-28T03:20:29.000Z","dependencies_parsed_at":null,"dependency_job_id":"b147e50b-f541-463f-9105-1bf7bc7de5e8","html_url":"https://github.com/navono/MLSimpleTutorial","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/navono/MLSimpleTutorial","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/navono%2FMLSimpleTutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/navono%2FMLSimpleTutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/navono%2FMLSimpleTutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/navono%2FMLSimpleTutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/navono","download_url":"https://codeload.github.com/navono/MLSimpleTutorial/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/navono%2FMLSimpleTutorial/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32097851,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-21T11:25:29.218Z","status":"ssl_error","status_checked_at":"2026-04-21T11:25:28.499Z","response_time":128,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["machine-learning","python3"],"created_at":"2024-11-17T11:21:47.982Z","updated_at":"2026-04-21T15:31:36.061Z","avatar_url":"https://github.com/navono.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 环境配置\nPython 3.5\n安装以下库：\n- scipy\n- numpy\n- matplotlib\n- pandas\n- sklearn\n\n还包括以下插件：\n- flak8\n- autopep8\n- pylint\n\n如果安装`scipy`失败的话，从[此处](http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy)上下载相应的包进行安装。如果需要`mkl`的话，从新从上述URL中下载`numpy + mkl`包安装。\n\n# 机器学习的一般步骤\n1. 定义问题（Define Problem）\n2. 准备训练数据（Prepare Data）\n3. 评估算法（Evaluate Algorithms）\n4. 改善算法结果（Improve Results）\n5. 显示算法结果（Present Results）\n\n\n# 步骤\n整体步骤：\n- Installing the Python and SciPy platform.\n- Loading the dataset.\n- Summarizing the dataset.（使用统计算法）\n- Visualizing the dataset.（使用plot）\n- Evaluating some algorithms.\n- Making some predictions.\n\n## Evaluating some algorithms\n步骤：\n1. Separate out a validation dataset.\n2. Set-up the test harness to use 10-fold cross validation.\n3. Build 5 different models to predict species from flower 4. 4. measurements\n5. Select the best model.\n\n模型：\n - 逻辑回归（Logistic Regression (LR)）\n - 线性判别分析（Linear Discriminant Analysis (LDA)）\n - K近邻（K-Nearest Neighbors (KNN)）\n - 分类与回归树（Classification and Regression Trees (CART)）\n - 高斯朴素贝叶斯（Gaussian Naive Bayes (NB)）\n - 支持向量机（Support Vector Machines (SVM)）\n\n LR和LDA属于线性算法，其他的属于非线性算法。","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnavono%2Fmlsimpletutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnavono%2Fmlsimpletutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnavono%2Fmlsimpletutorial/lists"}