In some cases, users might want to fit large models on large datasets using resample().
In this case here,
the person was unable to fit the models into RAM when specifying store_models = TRUE, but he still wanted access to the models, or rather the impurity scores from the models.
I have to suggestions to make these things more flexible:
Adding callbacks in resample() would allow to save these models to disk during resample().
Can we somehow score some measures that depend on the model on the workers? So that measures that require the model can still be evaluated when store_models = FALSE?
In some cases, users might want to fit large models on large datasets using
resample()
. In this case here, the person was unable to fit the models into RAM when specifyingstore_models = TRUE
, but he still wanted access to the models, or rather the impurity scores from the models.I have to suggestions to make these things more flexible:
resample()
would allow to save these models to disk duringresample()
.store_models = FALSE
?