Elastic Net Regression in scikit-learn: Balancing L1 and L2 Optimizations

Balancing L1 and L2 regularization has never been easier! Join us in mastering Elastic Net Regression with scikit-learn. Explore the intricacies of regularization, feature selection, and model performance enhancement. Enhance your data science skills and take your machine learning projects to the next level with this in-depth guide.
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As a full-time data analyst/scientist at a fintech company specializing in combating fraud within underwriting and risk, I've transitioned from my background in Electrical Engineering to pursue my true passion: data. In this dynamic field, I've discovered a profound interest in leveraging data analytics to address complex challenges in the financial sector.
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[@fabianaltendorfer11]

Very good video, thank you!

[@RyanNolanData]

Thank you for checking it out

[@rohantalaviya136]

great content

[@RyanNolanData]

Ty

[@adityasangave9703]

Do we need to standardize the categorical variables? I have heard most of times, there's no need to standardize and also it's logically incorrect to standardize and categorical variable as they are discrete. BTW love your content. Keep posting!!

[@736939]

7:08 I think fit_transform must be only on train set, for test there must be only transform.

[@RyanNolanData]

You are correct, mistake made within the video

[@ritamchatterjee8785]

​@@RyanNolanDataso basically what's the difference between fit and fit_transform