An epic journey through statistics and machine learning

Regularization Part 1: Ridge Regression

2 thoughts on “Regularization Part 1: Ridge Regression”

Hello Josh,

Thank you for your great videos!
One question: In your example, the data outliers (red dots) are arranged so that the slope of the red line is higher than the slope of the green data regression line (higher values of y for high values of x and smaller values of y for small values of x).
What if the slope of the red line is smaller than the slope of the green line (smaller values of y for high values of x and higher values of y for small values of x)? How does Ridge Regression work in this scenario?

Hello Josh,

Thank you for your great videos!

One question: In your example, the data outliers (red dots) are arranged so that the slope of the red line is higher than the slope of the green data regression line (higher values of y for high values of x and smaller values of y for small values of x).

What if the slope of the red line is smaller than the slope of the green line (smaller values of y for high values of x and higher values of y for small values of x)? How does Ridge Regression work in this scenario?

If ridge regression can not improve predictions by shrinking parameters, then it will do nothing at all.