Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [elastic-net]

A regularization method for regression models that combines the penalties of lasso and of ridge regression.

A regularization method for regression models that penalizes the size of regression coefficients $\beta_i$ and biases them towards zero. Elastic net includes two penalty terms, one proportional to $\sum |\beta_i|$ and another proportional to $\sum \beta_i^2$. When used alone, these penalty terms lead to Lasso regression and ridge regression respectively.

303 questions
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Imbalanced logistic elasticnet regression

I am performing a logistic elastic net regression to assess which variables influence the outcome and evaluate it. I am working with an imbalanced dataset that consists of 50 cases and 1700 controls. My objective is to assess the best approach for…
Xfar
  • 21
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Elastic Net Regression in R formula

This is not a question of how to do Elastic Net Regression in R, but understanding the objective function of Elastic Net Regression from the glmnet package. From the package itself the objective function is as follows found…
Jack Armstrong
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Should I use grid search with non-uniform steps to determine good $\alpha$ for elastic net?

Using the paramater names from What is lambda in an elastic net model (penalized regression)? I wonder about best practices or suggestions for how to determine $\alpha$, i.e. the paramater determining the weighting of the Lasso and the Ridge. My…
jonalv
  • 153
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What is the upper limit of the modulus of the coefficient calculated by elastic net regression?

If there is a Elastic-net criterion function: $$\mathcal{L}(\boldsymbol{\beta}) = \frac{1}{2}\sum_{n=1}^N(\boldsymbol{\beta}^{\top}\boldsymbol{x}_n - y_n)^2 + \frac{1}{2}\lambda(1-\eta)\|\boldsymbol{\beta}\|_2 +…
fei ma
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