Linked Questions
17 questions linked to/from Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to infinity?
19
votes
2answers
37k views
What is ridge regression? [duplicate]
I just need a simple explanation of what exactly ridge regression is so I can have a decent intuitive understanding of it. I understand it's about applying some sort of penalty to the regression ...
0
votes
1answer
103 views
Why is L2 regression good for handling multicollinearity? [duplicate]
Looking for an intuitive explanation, thanks.
0
votes
0answers
43 views
Ridge regression is similar to Linear regression [duplicate]
I can not see any difference between
Ridge Regression and Linear Regression
MY understanding, The point of ridge Regression is based on the training data we find the best line that fits training ...
1
vote
0answers
30 views
Ridge regression algorithm [duplicate]
Could someone explain how ridge regression algorithm works, step by step? Without focusing too much on formulas but rather how the mechanism works.
1
vote
0answers
8 views
59
votes
3answers
8k views
Why does ridge estimate become better than OLS by adding a constant to the diagonal?
I understand that the ridge regression estimate is the $\beta$ that minimizes residual sum of square and a penalty on the size of $\beta$
$$\beta_\mathrm{ridge} = (\lambda I_D + X'X)^{-1}X'y = \...
21
votes
2answers
927 views
The limit of “unit-variance” ridge regression estimator when $\lambda\to\infty$
Consider ridge regression with an additional constraint requiring that $\hat{\mathbf y}$ has unit sum of squares (equivalently, unit variance); if needed, one can assume that $\mathbf y$ has unit sum ...
19
votes
3answers
8k views
Can there be multiple local optimum solutions when we solve a linear regression?
I read this statement on one old true/false exam:
We can get multiple local optimum solutions if we solve a linear
regression problem by minimizing the sum of squared errors using
gradient ...
9
votes
1answer
5k views
Is Bayesian Ridge Regression another name of Bayesian Linear Regression?
I searched about Bayesian Ridge Regression on Internet but most of the result i became is about Bayesian Linear Regression. I wonder if it's both the same things because the formula look quite similar
5
votes
3answers
1k views
Can I use lasso when it is not a high dimensional setting?
I have 500 observations and 200 predictors, and I want to do the prediction while selecting some important features. I know that regularisation method (ridge, lasso) are shrinkage method for high-...
12
votes
2answers
2k views
Ridge penalized GLMs using row augmentation?
I've read that ridge regression could be achieved by simply adding rows of data to the original data matrix, where each row is constructed using 0 for the dependent variables and the square root of $k$...
7
votes
2answers
2k views
Bias / variance tradeoff math
I understand the matter in the underfitting / overfitting terms but I still struggle to grasp the exact math behind it. I've checked several sources (here, here, here, here and here) but I still don't ...
2
votes
0answers
639 views
How exactly does ridge regression helps in the case of multicollinearity?
I understand the reasoning behind ridge regression: we include some bias in the model in order to reduce the variance of the regression coefficients. My question is, why would we want to do that?
...
3
votes
0answers
208 views
Why is bridge regression called “bridge”?
Bridge regression coefficient estimate $\hat{β}^{br}$ are the values that minimize the
\begin{equation}
\text{RSS} + \lambda \sum_{j=1}^p|\beta_j|^q ,
\end{equation}
where $q \in \mathbb{R}$ and $q &...
2
votes
1answer
92 views
What modeling problem does ridge regression solve?
If your modeling problem is that you have too many features, a solution to this problem is LASSO regularization. By forcing some feature coefficients to be zero, you remove them, thus reducing the ...