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the next step upwards in the model structural hierarchy will combine multiple individual full runs of an LDA_TS model
we need to determine what we'll call it (mLDA_TS, LDA_TSs, ??), but this function will allow for multiple runs of the same model front-to-back but with perhaps different data, for example with leaving-out-cross validation approaches that work with evaluation of the full model at the end, rather than at each component.
this functionality will also allow for a more active decision making about each of the component models (see #146#147#148), allowing the user to for example decide on which topic model or time series model to use.
accordingly, we'll need a higher-level options list that controls the full model run, evaluation, and selection.
The text was updated successfully, but these errors were encountered:
we'll want to have some element at the top level and perhaps at lower levels in the model objects that indicates which data are used/not out of a whole data set
the next step upwards in the model structural hierarchy will combine multiple individual full runs of an
LDA_TS
modelwe need to determine what we'll call it (
mLDA_TS
,LDA_TSs
, ??), but this function will allow for multiple runs of the same model front-to-back but with perhaps different data, for example with leaving-out-cross validation approaches that work with evaluation of the full model at the end, rather than at each component.this functionality will also allow for a more active decision making about each of the component models (see #146 #147 #148), allowing the user to for example decide on which topic model or time series model to use.
accordingly, we'll need a higher-level options list that controls the full model run, evaluation, and selection.
The text was updated successfully, but these errors were encountered: