Well yes, I know how to actually partition the data. The problem is you can't throw a cell array of separate data sets into fitcensemble and have it calculate a kfold loss across the entire thing
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Explicit indices for k-fold partitioning
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Is there any way to explicity provide the indices of each partition in a k-fold partition? I'd like to find optimal hyperparameters, but all the methods seem to either sequentially or randomly divide up the data. My data evolves over time, where each time step has a different number of observations. Doing things either sequentially or randomly results in 'looking into the future'. I'd like the partitions to reflect the information I have up to that time, and predict the response for next time to obtain a kfoldloss.
(Time itself has no relevance however, so this isn't amenable to time-series type analysis. It's a classification problem)
thanks in advance
anthony
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Answers (1)
Adam Danz
on 11 Sep 2020
Edited: Adam Danz
on 14 Sep 2020
Perhaps something like
x = 1:100; % demo vector
k = 5; % 5-partitions
folds = cell(k,1);
for i = 1:k
folds{i} = x(i:k:end);
end
Though, those partitions are far from randomized but they maintain temporal order. To fix that, you could 1) create a grouping variable for each segment, randomize the segments, and the execute the loop above on the randomized segments.
Alternatively, you could use stratified sampling within subgroups using
but that only ensure that each group is represented equally, it will not maintain the temporal order of your data.
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