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It came up in #7136 that the dev docs are not super explicit what parameters should be passed to fit.
Basically, the rule is that should only be those that have shape n_samples and need to be sliced in cross-validation. The rest should go into __init__ (also maybe mention that cross-validation does allow this slicing).
For transform and predict we don't usually have parameters, though there could be times when that would be helpful, like thresholds for prediction / feature selection. We haven't really handled that consistently so far, maybe we should advise to generally avoid parameters to transform and predict, as people can always do estimator.set_params(stuff=1) or estimator.stuff = 1
The text was updated successfully, but these errors were encountered:
As said in #7156-comment documentation has changed since this issue was opened. I'm closing it. Feel free to open a new one if the issue is still present in the new version.
It came up in #7136 that the dev docs are not super explicit what parameters should be passed to
fit
.Basically, the rule is that should only be those that have shape
n_samples
and need to be sliced in cross-validation. The rest should go into__init__
(also maybe mention that cross-validation does allow this slicing).For
transform
andpredict
we don't usually have parameters, though there could be times when that would be helpful, like thresholds for prediction / feature selection. We haven't really handled that consistently so far, maybe we should advise to generally avoid parameters totransform
andpredict
, as people can always doestimator.set_params(stuff=1)
orestimator.stuff = 1
The text was updated successfully, but these errors were encountered: