younes-charfaoui / feature-selection-techniques

Python code source for features selection 👨‍🔬 series on medium website. 📰

Hands-on with Feature Selection Techniques 👩‍🔬

Get to know the features selection techniques in a hands-on way, Throughout the series, we’ll explore a range of different methods and techniques used to select the best set of features that will help you build a simpler, faster, and more reliable machine learning models.

Getting Started ✅ 💯

Here are the set of articles:

• An Introduction
• Filter Methods
• Wrapper Methods
• Embedded Methods
• Hybrid Methods
• More Advanced Methods

Prerequisites 📝

You should have the following librariesand softwares installed on your PC alongside with Python:

• Pandas
• Numpy
• Sklearn
• Matplotlib
• Jupyter notebook

Contributing 💡

If you want to contribute to this project and make it better with new ideas, your pull request is very welcomed. If you find any issue just make an issue in the repository issue section, thanks a lot.

Contact me Via 🙏

• Twitter
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• Facebook

License 📝

This project is licensed under the MIT License - see the LICENSE.md file for details

feature-selection-techniques's Issues

The scope of use of each methods

I have a question about the scope of use of the feature selection methods.
When using machine learning methods such as SVR, I have found the literature where the wrapper method has been introduced.
On the other hand, when using deep learning methods such as LSTM, I did not find any literature that introduced the wrapper method.
Should I use the filter method instead of the wrapper method or the embedding method because of the random bias in deep learning?