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)
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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.