Good video siddarth my question is always does the loss function reduces to a quadratic function in a multivariable system or higher order polynomial functions possible and how does lasso works in this case???? Since a quadratic loss / cost function becomes imperative for a linear regression model???
Where exactly is the math in the math behind Lasso ? I was expecting rigorous math proof on why Lasso drags some of the feature coefficients to zero while Ridge doesnt. Why lasso cannot be optimised by GD.
Best TH-cam channel to learn Machine learning concepts
I just love you. thanks for the great explanation.
Nice video!! Great explanation
Thank you!
Hi Sid. How will lasso come to know whether a particular feature is important or not before eliminating it!
Good video siddarth my question is always does the loss function reduces to a quadratic function in a multivariable system or higher order polynomial functions possible and how does lasso works in this case???? Since a quadratic loss / cost function becomes imperative for a linear regression model???
Plz starry Deep learning Bro as early as possible.. eagerly waiting
Sir take a breathe in between, delivery the content slowly, and your videos has much value, take it slow sir.
Where exactly is the math in the math behind Lasso ? I was expecting rigorous math proof on why Lasso drags some of the feature coefficients to zero while Ridge doesnt. Why lasso cannot be optimised by GD.
Have u got answer for ur question??
@@ganeshnageswaran4808 th-cam.com/video/XNd7SfG-_ho/w-d-xo.html