39k 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 ...
504 views

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

Looking for an intuitive explanation, thanks.
438 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 ...
32 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.
12k 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 = \...
2k 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 ...
11k 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 got is about Bayesian Linear Regression. I wonder if it's both the same things because the formula look quite similar
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### 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 ...
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### 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-...
3k 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 ...
3k 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 $k$...
746 views

### Why is Elastic Net called Elastic Net?

What is the etymology of "Elastic Net" in Elastic Net Regularization? Does it have anything to do with the name of "lasso"? Related: Why is ridge regression called "ridge&...
1k 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? ...
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 &...