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 Solvers10
Suggested Problems
-
313 Solvers
-
Project Euler: Problem 1, Multiples of 3 and 5
3664 Solvers
-
Replace all zeros and NaNs in a matrix with the string 'error'
103 Solvers
-
Cell Counting: How Many Draws?
2377 Solvers
-
143 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.