# Linked Questions

18 questions linked to/from Why does shrinkage work?
1answer
12k views

### What is penalized logistic regression [duplicate]

I need to do a logistic regression that will likely have a lot of zeros. Can someone explain penalized logistic regression to me like I'm dumb?
3answers
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### Why does regularization of coefficient magnitude improve the generalization of linear regression? [duplicate]

What is the basic argument upon which ridge and lasso regression are based on? I went through Tikhonov regularization wiki where it was mentioned that In many cases, tikhonov matrix is chosen as ...
0answers
248 views

### Why were 'Regularization' methods (Lasso, Ridge, Elastic Net) created in the first place? [duplicate]

What problem do regularization methods solve? I thought it was feature selection and to prevent overfitting. However, I was informed that the reason Ridge, Lasso, and Elastic Net were created in the ...
0answers
217 views

### Linear Regression — Regularization, shrinkage [duplicate]

Regularization reduces the magnitudes of the regression coefficients. I read that this helps reduce the variance of the model. Why exactly do smaller values of the coefficients lead to a lower ...
1answer
61 views

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

Looking for an intuitive explanation, thanks.
0answers
34 views

### LASSO method. Intuitively how does it select variables? [duplicate]

Intuitively how does the LASSO method select its variables? Is it based on standard econometrics?
5answers
4k views

### How can top principal components retain the predictive power on a dependent variable (or even lead to better predictions)?

Suppose I am running a regression $Y \sim X$. Why by selecting top $k$ principle components of $X$, does the model retain its predictive power on $Y$? I understand that from dimensionality-reduction/...
2answers
4k views

1answer
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### Why does Daniel Wilks (2011) say that principal component regression “will be biased”?

In Statistical Methods in the Atmospheric Sciences, Daniel Wilks notes that multiple linear regression can lead to problems if there are very strong intercorrelations among the predictors (3rd edition,...
2answers
356 views

### Why does shrinkage really work, what's so special about 0?

There is already a post on this site talking about the same issue: Why does shrinkage work? But, even though the answers are popular, I don't believe the gist of the question is really addressed. It ...
1answer
497 views

### Where is there bias-variance trade-off, and why?

In Wikipedia, the "Bias–variance tradeoff" is mentioned in the context of prediction models where one can control the complexity of the model with some tuning parameters, and the more complex the ...

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