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Ridge Regression (L2 Regularization)
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Description:
Linear regression is a powerful statistical tool for data analysis and machine learning. But when your hypothesis (model) uses a higher order polynomial, your model could overfit the data. One way to avoid such ovefitting is by using Ridge Regression, or L2 Regularization. It effectively adds a term to the cost function that limits the models parameters values. This is also sometimes referred to as shrinkage. ** SUBSCRIBE: https://www.youtube.com/c/EndlessEngineering?sub_confirmation=1 ** Linear Regression with gradient descent (Ordinary Least Squares) video: https://youtu.be/fkS3FkVAPWU ** Follow us on Instagram for more endless engineering: https://www.instagram.com/endlesseng/ ** Like us on Facebook: https://www.facebook.com/endlesseng/ ** Check us out on twitter: https://twitter.com/endlesseng
YouTube url:
https://www.youtube.com/watch?v=skOcLw_fXDs
Created:
25. 4. 2022 06:10:45