# Questions tagged [ridge-regression]

A regularization method for regression models that shrinks coefficients towards zero.

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### Dividing the weights obtained on an already standardized data set by the standard deviation of the features? (Ridge regression)

I'm trying to understand a code snippet from my lecture on Machine Learning (see the code below). It extracts the mean and standard deviation of the features and uses them to 'normalize' (...
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### Does ridge regression reduce the coefficients of the variables all the way to zero at very high penalty

I have read in one article that ridge regression doesn't reduce the coefficients of the features to zero where as in some other article I read that it can reduce the coefficients to zero when a very ...
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### Can ridge regression be used for feature selection?

I'm trying to figure out whether using Ridge Regression for regularization can be used to cause a more sparse hypothesis however to me it seems like ridge will never actually bring any coefficients to ...
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### Does it matter whether we put regularization parameter ($C$) with error or weight term in Kernel ridge regression?

Kernel ridge regression associate a regularization parameter $C$ with weight term ($\beta$): \$\text{Minimize}: {KRR}=C\frac{1}{2} \left \|\beta\right\|^{2} + \frac{1}{2}\sum_{i=1}^{\mathcal{N}}\left\|...
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### Running into strange errors when using glmnet and generating graphs

I developed a very simple Ridge Regression with glmnet, the R package. When I use the plot.glmnet() function I encounter strange errors: ...