[scikit-learn] Why ridge regression can solve multicollinearity?

[lampahome]

Stuart Reynolds <stuart at 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?
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