Lasso
A sparsification method for linear regression that adds an L1-norm regularization term to the loss function, causing the model to tend toward sparse solutions. It is commonly used for signal recovery in compressed sensing and for feature selection.
Timeline (last 90 days)
Elastic-Net is a regression method that combines L1 regularization (Lasso) with L2 regularization (Ridge).
Pure Lasso is unstable when facing highly correlated genes, often randomly selecting one gene from a group of correlated genes, while Ridge's L2 penalty allows correlated features to be retained together.
Ridge regression and Lasso can alleviate the coefficient instability caused by multicollinearity, but at the cost of the coefficients no longer being unbiased estimates.
When the prior is a uniform distribution, MAP degenerates into MLE; a Gaussian prior corresponds to Ridge regression, and a Laplace prior corresponds to Lasso regression
All Facts (4)
Elastic-Net is a regression method that combines L1 regularization (Lasso) with L2 regularization (Ridge).
50%UnverifiedPure Lasso is unstable when facing highly correlated genes, often randomly selecting one gene from a group of correlated genes, while Ridge's L2 penalty allows correlated features to be retained together.
50%UnverifiedRidge regression and Lasso can alleviate the coefficient instability caused by multicollinearity, but at the cost of the coefficients no longer being unbiased estimates.
50%UnverifiedWhen the prior is a uniform distribution, MAP degenerates into MLE; a Gaussian prior corresponds to Ridge regression, and a Laplace prior corresponds to Lasso regression
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