方法证据记录
Lasso Regression
Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.
源记录
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Least Absolute Shrinkage and Selection Operator (LASSO)
分类方法记录 · ml-model / machine-learning
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