https://github.com/samirpdx/PolishedCode_MachineLearning

This is a polished code example of my own implementation of a Coordinate Descent Algorithm with Elastic Net Regularization used for solving least-squares regression. This was done as part of my DATA 558 Machine Learning course at the University of Washington.
https://github.com/samirpdx/PolishedCode_MachineLearning

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This is a polished code example of my own implementation of a Coordinate Descent Algorithm with Elastic Net Regularization used for solving least-squares regression. This was done as part of my DATA 558 Machine Learning course at the University of Washington.

• Host: GitHub
• URL: https://github.com/samirpdx/PolishedCode_MachineLearning
• Owner: samirpdx
• Created: 2017-06-08T08:53:26.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-06-09T11:22:45.000Z (over 7 years ago)
• Last Synced: 2024-08-01T15:14:22.896Z (3 months ago)
• Language: Python
• Homepage:
• Size: 517 KB
• Stars: 2
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome - PolishedCode_MachineLearning - This is a polished code example of my own implementation of a Coordinate Descent Algorithm with Elastic Net Regularization used for solving least-squares regression. This was done as part of my DATA 558 Machine Learning course at the University of Washington. (Python)

README

        
PolishedCode_MachineLearning

=============================

Coordinate Descent Algorithm with Elastic Net Regularization

--------------------------------------------------------------

This is a polished code example of my own implementation of a Coordinate Descent Algorithm with Elastic Net 

Regularization used for solving least-squares regression for the minimization problem seen below:  



This package was created as a part of my DATA 558 Machine Learning course at the University of Washington.

For examples of implementation, please see 

the [examples](https://github.com/samirpdx/PolishedCode_MachineLearning/tree/master/examples) folder.  

No data files are required for download, as they are downloaded into the notebook via URLs.

For viewing the raw Python code for this implementation, please refer 

to [myelasticnet.py](https://github.com/samirpdx/PolishedCode_MachineLearning/blob/master/src/myelasticnet.py) 

Directory Structure

---------------------

```

PolishedCode_MachineLearning/

  |- examples/

     |- __init__.py

     |- README.md

     |- Polished Code - ElasticNet (Comparison with Sci-Kit Learn).ipynb

     |- Polished Code - ElasticNet (Real-World Example).ipynb

     |- Polished Code - ElasticNet (Simulated Example).ipynb

  |- images/

     |- elasticnet.jpg

  |- src/

     |- __init__.py

     |- myelasticnet.py

  |- README.md

  |- setup.py

```

Installation

---------------

_Note:  To run this package you will need familiarity with bash command line and Jupyter Notebook._

In a directory on your local machine, run the following `git` command in the bash terminal to clone the 

`PolishedCode_MachineLearning` repository onto your computer:

```

git clone https://github.com/samirpdx/PolishedCode_MachineLearning.git

```

Then in the bash terminal go to the the newly installed package folder:

```

cd PolishedCode_MachineLearning/

```

And install the package by running the `setup.py` file:

```

python setup.py install

```