On Wed, Jan 8, 2020 at 9:43 PM <josef.p...@gmail.com> wrote: > > > 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. >
e.g. Marquardt, Donald W., and Ronald D. Snee. "Ridge regression in practice." *The American Statistician* 29, no. 1 (1975): 3-20. I just went through it last week because of a argument about variance inflation factor in Ridge > > Josef > > > >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >
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