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[ENH] distribution transformations, calibration #321
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I am unsure if I understand it correctly. Should this be a transformer that calibrates the quantiles? E.g. like: https://scikit-learn.org/stable/modules/calibration.html |
Why should have
regarding these both bullet points, I assume that we need to discuss this in a meeting. I am not sure if I understand this correctly. In general, I think such a transformers would be very useful. |
Exactly!
I am considering the distribution objects, as inheriting from The
Sure - one of the dev meetings? It is probably not clear in this brevity, possibly I need to write an API design proposal. |
Discussion with @benHeid on probability calibration indicates that we may like to have another special category of transformations: distribution-to-distribution, possibly with a secondary input being samples.
Examples:
fit(X=y_proba, y_true)
, wherey_proba
are proba predictions, andy_true
is a calibration sample. Bothy_proba
andy_true
have 2D shape (N, d).fit(X).transform(X)
produces a distribution of same shape asX
. IfX
is assumed i.i.d. sample, the distribution estimated is scalar, or same shape as a row ofX
. Question is what the output should be, even if the "genuine" estimate is a scalar or row distribution. Perhaps a hybrid interface withestimate
- can be row, scalar - andtransform
- always array - can be helpful here.Empirical
by aQPD
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