Questions tagged [lasso]
A regularization method for regression models that shrinks coefficients towards zero, making some of them equal to zero. Thus lasso performs feature selection.
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Reducing Variance with Regularization in LOOCV for Small Datasets
I have a small dataset and I am considering using Leave-One-Out Cross-Validation (LOOCV) to evaluate my model. I understand that cross-validation, in general, is a method to assess a model's ...
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How to determine lambda for graphical lasso?
I am trying to figure out how to determine lambda for a graphical lasso.
I have found that someone had the exact same question that me 9 years ago. I was wondering if anything exists in R to determine ...
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Assessing Random Search Cross Validation: Tuning in ElasticNet with Large Feature Sets
I'm working on estimating an ElasticNet model for a large dataframe with over 100,000 variables, resulting in a well overidentified scenario. To tune my model, I've set up a grid of hyperparameters (...
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why does LASSO regression return unstandardized coefficients [closed]
I have more general questions that does not refer to a coding issue.
Why does LASSO regression require standardization of the predictors but return unstandardized coefficients (glmnet function - https:...
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AUC > 0.5 under null model following feature selection
I've been going over the output of a Monte Carlo model that simulates disease risk as a function of genotype. Under a null model of no disease risk, we have 1000 case and 1000 control individuals. ...
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penalized package [closed]
Has anyone used penalized package? I was using it for lasso in Cox regression, with time-varying coefficients.
The problem is when I made a plot with ...
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The sum of $O_p$ --$ O_p \left(s^2\frac{\log d}{n}+s\sqrt{\frac{\log d}{n}} \right) $
I read papers in the area of inference for high-dimensional graphical models and these papers always state the convergence rate of the estimator. Using $O_p$ is a good choice.
Maybe I made some ...
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What is the boundary curve for $λ_1$ and $λ_2$ that give at least a 0 component in elastic net?
Define the elastic net estimate:
$
\hat{\beta}^{\lambda_1, \lambda_2} = \arg \min_{\beta \in \mathbb{R}^p} \left( \frac{1}{2n} \| y - X\beta \|_2^2 + \lambda_1 \ \frac{1}{2} \|\beta \|_2^2 + \lambda_2 ...
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When does a extended BIC curve for a Gaussian Graphical model/GLasso look incorrect?
I have a model for a network, and I wanted to analyze the extended BIC curve for a graphical lasso model as according to Foygel and Drton 2010. The paper gives a list of assumptions for the data/model ...
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Unacceptable results for adj R2
I have a dataset with 19 features. When I ran it with the Lasso algorithm. R2 for test and train was 0.69. But the value of adj r2 for test is 1.28 (above 1), and for train the value is 0.28. What is ...
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Motivation for automated variable selection in case of p>n
I have written the following text as a motivation for using automated variable selection in cases where the number of variables (p) is greater than the number of observations (n). However, I am not ...
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Evaluating Lasso's Unique Solution and its consequences in applications?
I've grasped from a paper (https://www.stat.cmu.edu/%7Eryantibs/papers/lassounique.pdf) that Lasso may not yield a unique solution when the number of variables (p) exceeds the number of observations (...
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Behavior of Lasso Estimator with More Predictors Than Observations (p > n) and Identical Correlations?
What is the behavior of a Lasso estimator if it is used in a dataset with more predictors (p) than observations (n), where all predictors are uncorrelated but highly relevant to
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y with exactly the ...
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Constructing subgroup comparisons for variable selection
I am trying to construct a covariate according to the the following description:
When constructing the covariates to measure a subgroup effect, we
generate covariates that capture the causal effect ...
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Does penalizing the slope in Ridge/ Lasso regression has adverse effect based on the training data?
I have just started to learn ridge and lasso regression (by this YouTube video). To my understanding, these regressions are similar to linear regression, but we penalize the higher slope by the ...
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