I am trying to learn this and already I have several questions

1) Is it still true there is no (non bootstrap) way of generating SE for lasso? If so how do you do statistical test?

2) One article I read said that all variables had to be standardized to use bootstrap? Is that true?

3) I understand that lasso assigns a penalty to shrink various estimates. But I don't understand substantively which will shrink more than others (that is the basis they will shrink). I have to admit although I know of penalties in regression, I don't understand how they work.

For example on article says "The less important features of a dataset are penalized by the lasso regression. The coefficients of this dataset are made zero leading to their elimination. The dataset with high dimensions and correlation is well suited for lasso regression. "

How does the regression decide in practice what the less important features are minimized (I know an algorithm is used of course and about regularization).

1) Is it still true there is no (non bootstrap) way of generating SE for lasso? If so how do you do statistical test?

2) One article I read said that all variables had to be standardized to use bootstrap? Is that true?

3) I understand that lasso assigns a penalty to shrink various estimates. But I don't understand substantively which will shrink more than others (that is the basis they will shrink). I have to admit although I know of penalties in regression, I don't understand how they work.

For example on article says "The less important features of a dataset are penalized by the lasso regression. The coefficients of this dataset are made zero leading to their elimination. The dataset with high dimensions and correlation is well suited for lasso regression. "

How does the regression decide in practice what the less important features are minimized (I know an algorithm is used of course and about regularization).

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