On Wed, Jan 8, 2020 at 9:38 PM lampahome <pahome.c...@mirlab.org> wrote:
> > > Stuart Reynolds <stu...@stuartreynolds.net> 於 2020年1月9日 週四 上午10:33寫道： > >> Correlated features typically have the property that they are tending to >> be similarly predictive of the outcome. >> >> L1 and L2 are both a preference for low coefficients. >> If a coefficient can be reduced yet another coefficient maintains similar >> loss, the these regularization methods prefer this solution. >> If you use L1 or L2, you should mean and variance normalize your features. >> >> > You mean LASSO and RIDGE both solve multilinearity? > LASSO has the reputation not to be good when there is multicollinearity, that's why elastic net L1 + L2 was introduced, AFAIK With multicollinearity the length of the parameter vector, beta' beta, is too large and L2, Ridge shrinks it. Josef > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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