Linked Questions

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1answer
1k views

Non-Singularity due to inclusion of non-zero lambda in ridge regression [duplicate]

There were many similar questions on this site , related to this but none were exactly to the point I wanted to ask So the question is relates to ridge regression and This link where there is a ...
0
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1answer
57 views

Why is L2 regression good for handling multicollinearity? [duplicate]

Looking for an intuitive explanation, thanks.
0
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0answers
29 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 ...
68
votes
2answers
18k views

Why is ridge regression called “ridge”, why is it needed, and what happens when $\lambda$ goes to infinity?

Ridge regression coefficient estimate $\hat{\beta}^R$ are the values that minimize the $$ \text{RSS} + \lambda \sum_{j=1}^p\beta_j^2. $$ My questions are: If $\lambda = 0$, then we see that the ...
19
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2answers
35k 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 ...
10
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5answers
5k views

Ridge & LASSO norms

This post follows this one: Why does ridge estimate become better than OLS by adding a constant to the diagonal? Here is my question: As far as I know, ridge regularization uses a $\ell_2$-norm (...
5
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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-...
7
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1answer
4k 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
9
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2answers
932 views

Lucid explanation for “numerical stability of matrix inversion” in ridge regression and its role in reducing overfit

I understand that we can employ regularization in a least squares regression problem as $$\boldsymbol{w}^* = \operatorname*{argmin}_w \left[ (\mathbf y-\mathbf{Xw})^T(\boldsymbol{y}-\mathbf{Xw}) + \...
4
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1answer
1k views

What is the significance of a linear dependency in a polynomial regression?

I'm trying to find the best polynomial regression for a dataset where the polynomial's power is between 2 and 10. So the regression can have an x10 term at most in it. The dataset itself is simply a ...
2
votes
1answer
720 views

3D surface plot for least square & ridge regression

I'm very impressed by this plot: Why does ridge estimate become better than OLS by adding a constant to the diagonal? Does someone has any clue about how to plot this on R? I mean, how to get RSS ...
2
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0answers
523 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? ...
1
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0answers
471 views

Variable importance in cases of multicollinearity: OLS vs ridge regression

I have read that when using Ordinary Least Squares (OLS) for multiple linear regression, the coefficients/weights are unreliable for predictor variables that are collinear. I was wondering if this is ...