Open RAP1989 opened 5 years ago
Thanks, I'm not familiar with the timeslice method from caret
. Does this create mutually exclusive blocks of observations (rows)? If that is the case, you could use hte validRows
list within the SuperLearner.CV.control
function to set the data splits.
Thank you so much for the response. Unfortunately, the data splits are not mutually exclusive, as in https://topepo.github.io/caret/data-splitting.html#data-splitting-for-time-series the indices for the train data do overlap.
Hello ecpolley, I also want to create blocks of rows of series and I tried the command SuperLearner.CV.control
. Lets assume my series has 250 entries (rows) and I want to create blocks from 1:50, 51:100, 101:150, 151:200, and 201:250. So I created a list with 5 entries and where each entry corresponds to the aforementioned sequences. However, after using CV.SuperLearner
, I got NA as se for every algorithm in the summary. Did I do something wrong?
@bm2609 Does it work with SuperLearner
?
CV.SuperLearner
is more complicated as it requires specifications of the nested cross-validation folds/
Yes, it works with SuperLearner
. Thank you!
Hi,
Since SuperLearner integrates with caret, I tried to search for a possibility to use the resampling method "timeslice" (for time series) instead of cross validation. However, it seems that only CV is possible in SuperLearner and I haven't found any ways for changing it. SuperLearner.control and SuperLearner.CV.Control don't seem to have this kind of possibility, for example.
Have I overlooked something? If the resampling method cannot be changed, are there any plans in the future for implementing timeslice in SuperLearner? Thanks