Fully Understand ElasticNet Regression with Python

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.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI


AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.