Monday, October 18, 2004
Maybe you can try other values of lambda and find the best one. :)
Yes, if the one standard error rule is not behaving sensibly, that's what I would do.
After feeding the normalizied feature values into ridge algorithm, we predict the values y'= (y-mean(y in the training set)/sqrt(variance(y in the training set))). Should we get y from y' by the reverse calculation? Thanks.
Exactly right - invert the standardization to get the predictions on the right scale. Note you don't have to standardize y but if you do you need to this inversion. D.
Yes, if the one standard error rule is not behaving sensibly, that's what I would do.
After feeding the normalizied feature values into ridge algorithm, we predict the values y'= (y-mean(y in the training set)/sqrt(variance(y in the training set))). Should we get y from y' by the reverse calculation? Thanks.
Exactly right - invert the standardization to get the predictions on the right scale. Note you don't have to standardize y but if you do you need to this inversion. D.
