Given a predictor data matrix X of size 
, target variable vector y of size 
and a shrinkage factor λ (scalar) (ridge regularization), write the function to compute linear regression model coefficients β 
to model the data. The data has n observations, p predictor variables in the X matrix
, target variable vector y of size and a shrinkage factor λ (scalar) (ridge regularization), write the function to compute linear regression model coefficients β to model the data. The data has n observations, p predictor variables in the X matrix The model is defines as: 
where sigma is gaussian noise.
where sigma is gaussian noise.(Hint: search on google for closed form solution of a linear regression problem)
Solution Stats
Problem Comments
2 Comments
Solution Comments
Show comments
Loading...
Problem Recent Solvers13
Suggested Problems
-
14420 Solvers
-
Renaming a field in a structure array
1582 Solvers
-
Create a vector whose elements depend on the previous element
808 Solvers
-
129 Solvers
-
Check that number is whole number
5522 Solvers
Problem Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!
Yuvraj, this problem looks interesting, and I was looking forward to learning about ridge regularization. I suggest that you remove the code from the function template: you are giving us the answer! Also, several Cody players have recommended at least four tests to discourage lookup table solutions and other cheats.
The test suite's incorrect: the matrix inversion in the ridge estimator is missing.