Closed sbrockhaus closed 7 years ago
@sbrockhaus I have the slight feeling that this warning is nonsense. I can remember that we did some experiments and found out that df2lambda becomes instable. However, as of today, I cannot reproduce this finding. Did you ever have the feeling that something went wrong here? If not, I would remove this peace of code and close this issue.
@fabian-s What is your feeling? You requested an option once to be able to skip this test.
Sorry, I don't have any intuition about this. If your tests don't reproduce the issue this warns about then it should be safe to remove it, no?
For me, it was just odd that for the same model the warning is given or not given depending on how the model is built. I have usually ignored the warning anyway and never had the feeling that I had any troubles because of that.
The warning in
df2lambda()
, line https://github.com/hofnerb/mboost/blob/master/pkg/mboostPatch/R/bl.R#L25gives a warning if the design matrix
X
contains absolute values > 10.If
%O%
is used to kronecker two base-learners, this warning is never given. The reason is that in this caseX
is only a dummy-matrix and not the real design matrix, see the call todf2lambda()
within%O%
in line https://github.com/hofnerb/mboost/blob/master/pkg/mboostPatch/R/bkronecker.R#L57 whereXtX
is the squared design matrix. That means that one would have to checkXtX
instead of checkingX
.See a MWE on how two model fits using the same desing-matrix give or do not give a warning