Elastic Net CV

The elastic net is a regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods. The loss function is given as below. I used cyclic coordinate descent algorithm to implement this regression method.

In this repo, I have added following files in 'src' directory:

Elastic_Net.py:

This is the source code file where I have implemented my own coordinate descent algorithm to solve least-squares regression with elastic net regularization.

demo_on_real_dataset.py:

This is the demo file that allows the user to launch ElasticNet method on a real world dataset(Hitters).

demo_on_simulated_dataset.py:

This is the demo file that allows the user to launch ElasticNet method on a real world dataset(Hitters).

compare_with_sklearn.py

This is the file that allows user to perform comparison between my implementation and scikit-learns ElasticNetCV on a real world dataset(Hitters)

To run the above files, clone this repo and make sure following packages are installed on the system.

• pandas
• numpy
• sklearn
• matplotlib.pyplot
• sklearn.cross_validation
• sklearn.linear_model.ElasticNet
• sklearn.linear_model.ElasticNetCV