Linked Questions

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 ...

Looking for an intuitive explanation, thanks.

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 ...

Could someone explain how ridge regression algorithm works, step by step? Without focusing too much on formulas but rather how the mechanism works.

What is it called ridge regression? Where's the ridge?

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 = \...

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 ...

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 ...

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

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-...

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 $k$...

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 ...

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 &...

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 ...