Fully Understand ElasticNet Regression with Python

Author(s): Amit Chauhan

Originally published on Towards AI.

Regularization method in machine learning
Photo by Boitumelo on Unsplash

In simple terms, the elastic net regression took the qualities of ridge and lasso regression to regularize the machine learning regression model.

Where do we use elastic net regression?

It helps to overcome the issues of over-fitting with ridge quality.Dealing with multi-collinearity issues in the data.Reducing features in the data with lasso quality.

Before learning elastic net, we need to revise the main algorithm concept. To do a bias-variance trade-off for reducing the over-fit issue, we can use some methods like bagging, boosting, and regularization.

Over-fitting: The model is done on training data but not well on testing data. In… Read the full blog for free on Medium.

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Published via Towards AI