[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-agconti--kaggle-titanic":3,"tool-agconti--kaggle-titanic":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":80,"owner_email":81,"owner_twitter":78,"owner_website":82,"owner_url":83,"languages":84,"stars":93,"forks":94,"last_commit_at":95,"license":96,"difficulty_score":23,"env_os":97,"env_gpu":98,"env_ram":98,"env_deps":99,"category_tags":110,"github_topics":111,"view_count":23,"oss_zip_url":78,"oss_zip_packed_at":78,"status":16,"created_at":116,"updated_at":117,"faqs":118,"releases":159},1339,"agconti\u002Fkaggle-titanic","kaggle-titanic","A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques. ","kaggle-titanic 是一份面向初学者的泰坦尼克号生存预测实战教程，用一份完整的 IPython Notebook 手把手带你完成 Kaggle 经典入门赛。它把“如何从乘客姓名、舱位、性别、年龄等杂乱数据里找出谁更可能活下来”这一实际问题，拆解成数据清洗、可视化、特征工程、模型训练与评估的每一步，并给出可直接提交的预测结果。\n\n如果你刚接触数据科学、想用 Python 快速上手 Kaggle 竞赛，或者希望看到 Pandas + Matplotlib + Scikit-learn 等 PyData 全家桶的完整示范，这份 Notebook 会是很好的起点。亮点在于：用逻辑回归、SVM 和随机森林三种常见算法对比效果，配合交叉验证和可视化，让模型表现一目了然；所有代码和说明都写在 Notebook 里，浏览器即可查看，本地也能一键复现。","### Kaggle-titanic\nThis is a tutorial in an IPython Notebook for the Kaggle competition, Titanic Machine Learning From Disaster. The goal of this repository is to provide an example of a competitive analysis for those interested in getting into the field of data analytics or using python for Kaggle's Data Science competitions .\n\n**Quick Start:** [View](http:\u002F\u002Fnbviewer.ipython.org\u002Furls\u002Fraw.github.com\u002Fagconti\u002Fkaggle-titanic\u002Fmaster\u002FTitanic.ipynb) a static version of the notebook in the comfort of your own web browser.\n\n### Installation:\n\nTo run this notebook interactively:\n\n1. Download this repository in a zip file by clicking on this [link](https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic\u002Farchive\u002Fmaster.zip) or execute this from the terminal:\n`git clone https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic.git`\n2. Install [virtualenv](http:\u002F\u002Fvirtualenv.readthedocs.org\u002Fen\u002Flatest\u002Finstallation.html).\n3. Navigate to the directory where you unzipped or cloned the repo and create a virtual environment with `virtualenv env`.\n4. Activate the environment with `source env\u002Fbin\u002Factivate`\n5. Install the required dependencies with `pip install -r requirements.txt`.\n6. Execute `ipython notebook` from the command line or terminal.\n7. Click on `Titanic.ipynb` on the IPython Notebook dasboard and enjoy!\n8. When you're done deactivate the virtual environment with `deactivate`.\n\n\n#### Dependencies:\n* [NumPy](http:\u002F\u002Fwww.numpy.org\u002F)\n* [IPython](http:\u002F\u002Fipython.org\u002F)\n* [Pandas](http:\u002F\u002Fpandas.pydata.org\u002F)\n* [SciKit-Learn](http:\u002F\u002Fscikit-learn.org\u002Fstable\u002F)\n* [SciPy](http:\u002F\u002Fwww.scipy.org\u002F)\n* [StatsModels](http:\u002F\u002Fstatsmodels.sourceforge.net\u002F)\n* [Patsy](http:\u002F\u002Fpatsy.readthedocs.org\u002Fen\u002Flatest\u002F)\n* [Matplotlib](http:\u002F\u002Fmatplotlib.org\u002F)\n\n\n### Kaggle Competition | Titanic Machine Learning from Disaster\n\n>The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew.  This sensational tragedy shocked the international community and led to better safety regulations for ships.\n\n>One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew.  Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n\n>In this contest, we ask you to complete the analysis of what sorts of people were likely to survive.  In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.\n\n>This Kaggle Getting Started Competition provides an ideal starting place for people who may not have a lot of experience in data science and machine learning.\"\n\nFrom the competition [homepage](http:\u002F\u002Fwww.kaggle.com\u002Fc\u002Ftitanic-gettingStarted).\n\n### Goal for this Notebook:\nShow a simple example of an analysis of the Titanic disaster in Python using a full complement of PyData utilities. This is aimed for those looking to get into the field or those who are already in the field and looking to see an example of an analysis done with Python.\n\n#### This Notebook will show basic examples of:\n#### Data Handling\n*   Importing Data with Pandas\n*   Cleaning Data\n*   Exploring Data through Visualizations with Matplotlib\n\n#### Data Analysis\n*    Supervised Machine learning Techniques:\n    +   Logit Regression Model\n    +   Plotting results\n    +   Support Vector Machine (SVM) using 3 kernels\n    +   Basic Random Forest\n    +   Plotting results\n\n#### Valuation of the Analysis\n*   K-folds cross validation to valuate results locally\n*   Output the results from the IPython Notebook to Kaggle\n\n\n### Benchmark Scripts\nTo find the basic scripts for the competition benchmarks look in the \"Python Examples\" folder. These scripts are based on the originals provided by Astro Dave but have been reworked so that they are easier to understand for new comers.\n\nCompetition Website: http:\u002F\u002Fwww.kaggle.com\u002Fc\u002Ftitanic-gettingStarted\n","### Kaggle-泰坦尼克号\n这是一个针对Kaggle竞赛“泰坦尼克号：从灾难中学习机器学习”的IPython Notebook教程。本仓库的目标是为那些希望进入数据分析领域或使用Python参加Kaggle数据科学竞赛的人提供一个具有竞争力的分析示例。\n\n**快速入门：** [查看](http:\u002F\u002Fnbviewer.ipython.org\u002Furls\u002Fraw.github.com\u002Fagconti\u002Fkaggle-titanic\u002Fmaster\u002FTitanic.ipynb) 在您自己的网页浏览器中直接浏览该Notebook的静态版本。\n\n### 安装：\n\n要交互式运行此Notebook：\n\n1. 通过点击此[链接](https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic\u002Farchive\u002Fmaster.zip)下载本仓库的zip文件，或在终端中执行：\n   `git clone https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic.git`\n2. 安装[virtualenv](http:\u002F\u002Fvirtualenv.readthedocs.org\u002Fen\u002Flatest\u002Finstallation.html)。\n3. 进入您解压或克隆仓库的目录，并创建一个虚拟环境：`virtualenv env`。\n4. 激活该环境：`source env\u002Fbin\u002Factivate`。\n5. 使用`pip install -r requirements.txt`安装所需的依赖项。\n6. 在命令行或终端中执行`ipython notebook`。\n7. 在IPython Notebook的仪表板上点击`Titanic.ipynb`，即可开始体验！\n8. 使用完毕后，通过`deactivate`关闭虚拟环境。\n\n\n#### 依赖项：\n* [NumPy](http:\u002F\u002Fwww.numpy.org\u002F)\n* [IPython](http:\u002F\u002Fipython.org\u002F)\n* [Pandas](http:\u002F\u002Fpandas.pydata.org\u002F)\n* [SciKit-Learn](http:\u002F\u002Fscikit-learn.org\u002Fstable\u002F)\n* [SciPy](http:\u002F\u002Fwww.scipy.org\u002F)\n* [StatsModels](http:\u002F\u002Fstatsmodels.sourceforge.net\u002F)\n* [Patsy](http:\u002F\u002Fpatsy.readthedocs.org\u002Fen\u002Flatest\u002F)\n* [Matplotlib](http:\u002F\u002Fmatplotlib.org\u002F)\n\n\n### Kaggle竞赛 | 泰坦尼克号：从灾难中学习机器学习\n\n> RMS泰坦尼克号的沉没是历史上最臭名昭著的海难之一。1912年4月15日，泰坦尼克号在其处女航中与冰山相撞而沉没，导致2224名乘客和船员中有1502人遇难。这场轰动一时的悲剧震惊了国际社会，并促使各国出台了更为严格的船舶安全法规。\n\n> 这场海难造成如此重大伤亡的原因之一是船上配备的救生艇数量不足以容纳所有乘客和船员。尽管在幸存过程中也存在一定的运气成分，但某些群体的生存几率确实高于其他群体，例如妇女、儿童以及上层阶级。\n\n> 在本次竞赛中，我们要求您完成对哪些人群更有可能幸存的分析。具体而言，我们希望您运用机器学习工具来预测哪些乘客在这场悲剧中幸存了下来。\n\n> 这项Kaggle入门级竞赛为那些可能缺乏数据科学和机器学习经验的人提供了一个理想的起点。\n\n\n摘自竞赛[主页](http:\u002F\u002Fwww.kaggle.com\u002Fc\u002Ftitanic-gettingStarted)。\n\n### 本Notebook的目标：\n展示一个使用全套PyData工具库，以Python进行泰坦尼克号灾难分析的简单示例。这主要面向希望进入该领域的人，以及已经身处该领域并希望了解一个用Python完成的分析案例的人。\n\n#### 本Notebook将展示以下基础示例：\n#### 数据处理\n*   使用Pandas导入数据\n*   数据清洗\n*   通过Matplotlib进行可视化探索数据\n\n#### 数据分析\n*    监督式机器学习技术：\n    +   逻辑回归模型\n    +   结果绘图\n    +   使用三种核函数的支持向量机（SVM）\n    +   基础随机森林\n    +   结果绘图\n\n#### 分析评估\n*   使用K折交叉验证对本地结果进行评估\n*   将IPython Notebook中的结果输出至Kaggle\n\n\n### 基准脚本\n如需查找竞赛基准的基本脚本，请参阅“Python示例”文件夹。这些脚本基于Astro Dave提供的原始代码，但经过重新编写，以便新手更容易理解。\n\n竞赛官网：http:\u002F\u002Fwww.kaggle.com\u002Fc\u002Ftitanic-gettingStarted","# kaggle-titanic 快速上手指南\n\n## 环境准备\n- 系统：Windows \u002F macOS \u002F Linux 均可  \n- Python：3.7+（推荐 Anaconda 或 Miniconda）  \n- 网络：可访问 GitHub 与 PyPI（国内用户可配置清华\u002F阿里镜像加速）\n\n## 安装步骤\n1. 克隆仓库  \n   ```bash\n   git clone https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic.git\n   cd kaggle-titanic\n   ```\n\n2. 创建并激活虚拟环境  \n   ```bash\n   # 使用 venv（Python 自带）\n   python -m venv env\n   source env\u002Fbin\u002Factivate  # Windows 用 env\\Scripts\\activate\n   ```\n\n3. 安装依赖（国内镜像示例）  \n   ```bash\n   pip install -r requirements.txt -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n   ```\n\n4. 启动 Jupyter Notebook  \n   ```bash\n   jupyter notebook\n   ```\n   浏览器会自动打开，点击 `Titanic.ipynb` 即可开始学习。\n\n## 基本使用\n- **静态预览**：直接访问 [nbviewer 在线版本](http:\u002F\u002Fnbviewer.ipython.org\u002Furls\u002Fraw.github.com\u002Fagconti\u002Fkaggle-titanic\u002Fmaster\u002FTitanic.ipynb) 无需安装。  \n- **交互运行**：按上述步骤启动 Notebook 后，逐格运行代码即可复现 Titanic 生存预测全流程（数据清洗 → 可视化 → 逻辑回归\u002FSVM\u002F随机森林 → 交叉验证 → 生成提交文件）。","小赵是一名刚转岗到数据分析团队的运营专员，公司要求他两周内提交一份“用户流失预测”原型，以评估是否投入正式建模。他只有 Python 基础，从未做过机器学习项目。\n\n### 没有 kaggle-titanic 时\n- 面对 10 多个 CSV 文件，小赵用 Excel 拼接，手动删空值，三天过去才整理好训练集。  \n- 不知道特征工程怎么做，只能凭直觉把“最近登录天数”和“消费金额”直接喂给模型，结果 AUC 只有 0.52。  \n- 想画个分布图，却卡在 Matplotlib 语法，网上搜到的代码跑不通，浪费半天调试。  \n- 模型调参靠“感觉”，每次改完都要重新跑全量数据，本地风扇狂转，效率极低。  \n- 最终输出格式不符合业务系统要求，又花一天写脚本把 CSV 转成 JSON，领导已催了三次。\n\n### 使用 kaggle-titanic 后\n- 跟着 notebook 的 Pandas 示范，小赵 30 分钟就把多张表合并、缺失值填补完毕，代码直接复用。  \n- 参考教程里的特征工程套路（如分箱、交叉特征），他把“登录天数”切成高\u002F中\u002F低三档，AUC 立刻提升到 0.78。  \n- notebook 里现成的 Matplotlib 模板一键出图，分布、箱线图、相关性热力图瞬间生成，汇报 PPT 直接截图。  \n- 教程内置 K 折交叉验证与随机森林调参示例，小赵照抄 GridSearchCV，半小时找到最优参数，CPU 不再空转。  \n- 最终预测结果按 notebook 的 `to_csv('submission.csv', index=False)` 格式输出，业务系统无缝读取，提前一天交差。\n\nkaggle-titanic 让小赵用一份现成的泰坦尼克生存预测模板，快速复刻到用户流失场景，两周任务两天完成。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fagconti_kaggle-titanic_631ec8e9.png","agconti","Andrew Conti","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fagconti_39a35710.jpg",null,"@reactive-streaming ","Brooklyn, NY","andrew.g.conti@gmail.com","www.reactive.live","https:\u002F\u002Fgithub.com\u002Fagconti",[85,89],{"name":86,"color":87,"percentage":88},"Jupyter Notebook","#DA5B0B",97.2,{"name":90,"color":91,"percentage":92},"Python","#3572A5",2.8,949,677,"2026-04-05T01:30:00","Apache-2.0","Linux, macOS, Windows","未说明",{"notes":100,"python":98,"dependencies":101},"使用 virtualenv 创建隔离环境；通过 pip install -r requirements.txt 安装依赖；运行 ipython notebook 后打开 Titanic.ipynb 即可交互式查看；完成后执行 deactivate 退出虚拟环境",[102,103,104,105,106,107,108,109],"NumPy","IPython","Pandas","SciKit-Learn","SciPy","StatsModels","Patsy","Matplotlib",[13],[112,67,113,114,115],"machine-learning","kaggle-competition","ipython-notebook","python","2026-03-27T02:49:30.150509","2026-04-06T07:14:48.014938",[119,124,129,134,139,144,149,154],{"id":120,"question_zh":121,"answer_zh":122,"source_url":123},6115,"提交到 Kaggle 时报错“Survived 列只能为 0 或 1”，如何处理？","模型输出的概率值需要设置阈值转换为 0\u002F1。将预测概率 ≥ 0.5 设为 1，\u003C 0.5 设为 0；或者直接以 0 为阈值即可通过 Kaggle 校验。","https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic\u002Fissues\u002F18",{"id":125,"question_zh":126,"answer_zh":127,"source_url":128},6116,"Kaggle 官方数据集的列名现在全部大写，导致代码报错怎么办？","已有人提交 PR 适配大写列名，可直接合并 PR #9；或手动把代码里所有列名改为首字母大写（如 PassengerId、Survived、Pclass…）。","https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic\u002Fissues\u002F8",{"id":130,"question_zh":131,"answer_zh":132,"source_url":133},6117,"绘图时子图 Y 轴重复，如何只保留最左侧刻度？","在调用 df.plot() 时传入 sharey=True，即可让所有子图共享 Y 轴，消除冗余刻度。","https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic\u002Fissues\u002F7",{"id":135,"question_zh":136,"answer_zh":137,"source_url":138},6118,"为什么不用简单的逻辑回归，而用随机森林\u002FSVM？","虽然逻辑回归可行，但随机森林和支持向量机在 Titanic 数据集上的公开排行榜得分更高，通常能获得更好的预测精度。","https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic\u002Fissues\u002F25",{"id":140,"question_zh":141,"answer_zh":142,"source_url":143},6119,"README 把 SVM 和随机森林写成“无监督学习”，正确吗？","不正确。SVM 和随机森林都属于监督学习算法，README 已修正分类。","https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic\u002Fissues\u002F4",{"id":145,"question_zh":146,"answer_zh":147,"source_url":148},6120,"如何进一步利用 Cabin（舱位）信息提升成绩？","可把 Cabin 解析出“室友数量”——同一舱号的人数。将缺失值视为 0 后，该特征在热力图中显示出较高相关性，加入模型通常能提升效果。","https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic\u002Fissues\u002F28",{"id":150,"question_zh":151,"answer_zh":152,"source_url":153},6121,"requirements.txt 中的库版本太旧，如何获得兼容版本列表？","目前官方未更新，建议手动锁定常用版本：pandas==0.24.2、scikit-learn==0.20.3、matplotlib==3.0.3、seaborn==0.9.0（Python 2.7 环境）。","https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic\u002Fissues\u002F27",{"id":155,"question_zh":156,"answer_zh":157,"source_url":158},6122,"运行环境必须是 Python 2.7 吗？","是的，原仓库基于 Python 2.7 编写。README 已补充说明，请使用 2.7 环境以避免兼容性问题。","https:\u002F\u002Fgithub.com\u002Fagconti\u002Fkaggle-titanic\u002Fissues\u002F24",[160],{"id":161,"version":162,"summary_zh":78,"released_at":163},105705,"v0.2.0","2015-12-20T18:48:39"]