Eliminate Multicollinearity using Lasso Regression

Lasso regression adds L1-norm penalty/regularization term to the cost function to reduce model complexity & prevent over-fitting which may result from simple linear regression.

So is my new video only gonna explain how the constraint surface for lasso regression is pointy, but the one for ridge regression is round?

In this video, I’ll show you that if you have high multicollinearity in your features, then by applying Lasso Regression you can shrink the coefficients of some of the unwanted features to 0 thus eliminating multicollinearity.

I hope you all like it 🙂

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