@sebastian At 13:20, why is the solution between the global minimum and the penalty minimum lie somewhere where one of the weights is zero. In other words, why it should lie at the corner of the penalty function not just at the line. between the global minimum and the penalty minimum.
@sebastian I think using Logistic Regression directly for feature selection based on respective weights/coefficients means we are assuming all dimensions/features are independent. I understand this is not the correct way to do this. Pls advise.
@sebastian At 13:20, why is the solution between the global minimum and the penalty minimum lie somewhere where one of the weights is zero. In other words, why it should lie at the corner of the penalty function not just at the line. between the global minimum and the penalty minimum.
@sebastian amazing video. Thanks for sharing. I am getting deeper understanding of these topics with your videos.
@sebastian I think using Logistic Regression directly for feature selection based on respective weights/coefficients means we are assuming all dimensions/features are independent. I understand this is not the correct way to do this. Pls advise.
Yes, this assumption is correct. ML is full of trade-offs 😅. If you cannot make this assumption, I recommend the sequential feature selection approach
Thanks for pointing this out!