• 设为首页
  • 点击收藏
  • 手机版
    手机扫一扫访问
    迪恩网络手机版
  • 关注官方公众号
    微信扫一扫关注
    迪恩网络公众号
登陆 注册
描述文字

anujdutt9/Feature-Selection-for-Machine-Learning: Methods with examples for Feat ...

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称（OpenSource Name）：

anujdutt9/Feature-Selection-for-Machine-Learning

开源软件地址(OpenSource Url)：

https://github.com/anujdutt9/Feature-Selection-for-Machine-Learning

开源编程语言(OpenSource Language)：

Jupyter Notebook 100.0%

开源软件介绍(OpenSource Introduction)：

Feature Selection for Machine Learning

This repository contains the code for three main methods in Machine Learning for Feature Selection i.e. Filter Methods, Wrapper Methods and Embedded Methods. All code is written in Python 3.

Status: Ongoing

Requirements

1. Python 3.5 +

2. Jupyter Notebook

3. Scikit-Learn

4. Numpy [+mkl for Windows]

5. Pandas

6. Matplotlib

7. Seaborn

8. mlxtend

Datasets

1. Santander Customer Satisfaction Dataset

2. BNP Paribas Cardif Claims Management Dataset

3. Titanic Disaster Dataset

4. Housing Prices Dataset

Filter Methods

S.No. Name About Status
1. Constant Feature Elimination This notebook explains how to remove the constant features during pre-processing step. Completed
2. Quasi-Constant Feature Elimination This notebook explains how to get the Quasi-Constant features and remove them during pre-processing. Completed
3. Duplicate Features Elimination This notebook explains how to find the duplicate features in a dataset and remove them. Completed
4. Correlation This notebook explains how to get the correlation between features and between features and target and choose the best features. Completed
5. Machine Learning Pipeline This notebook explains how to use all the above methods in a ML pipeline with performance comparison. Completed
6. Mutual Information This notebook explains the concept of Mutual Information using classification and Regression to find the best features from a dataset. Completed
7. Fisher Score Chi Square This notebook explains the concept of Fisher Score chi2 for feature selection. Completed
8. Univariate Feature Selection This notebook explains the concept of Univariate Feature Selection using Classification and Regression. Completed
9. Univariate ROC/AUC/MSE This notebook explains the concept of Univariate Feature Selection using ROC AUC scoring. Completed
10. Combining all Methods This notebook compares the combined performance of all methods explained. Completed

Wrapper Methods

S.No. Name About Status
1. Step Forward Feature Selection This notebook explains the concept of Step Forward Feature Selection. Completed
2. Step Backward Feature Selection This notebook explains the concept of Step Backward Feature Selection. Completed
3. Exhaustive Search Feature Selection This notebook explains the concept of Exhaustive Search Feature Selection. Completed

Embedded Methods

S.No. Name About Status



鲜花
握手
雷人
路过
鸡蛋
该文章已有0人参与评论

请发表评论

全部评论

专题导读
More+
上一篇：
python-engineer/ml-study-plan: The Ultimate FREE Machine Learning Study Plan发布时间：2022-08-18
下一篇：
jadijadi/machine_learning_with_python_jadi: The notebooks we use on ML course发布时间：2022-08-18
热门推荐
More+
热门话题
More+
阅读排行榜

扫描微信二维码

查看手机版网站

随时了解更新最新资讯

139-2527-9053

在线客服（服务时间 9:00～18:00）

在线QQ客服
地址：深圳市南山区西丽大学城创智工业园
电邮：jeky_zhao#qq.com
移动电话：139-2527-9053

Powered by 互联科技 X3.4© 2001-2213 极客世界.|Sitemap