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[MRG+2] TransformedTargetRegressor #9041
Despite the multitude of comments, overall I think this is what we want. Good work
As long as the value of having inverse_func is clear in the docs, I don't mind…
On 8 Jun 2017 9:13 pm, "Guillaume Lemaitre" ***@***.***> wrote: ***@***.**** commented on this pull request. ------------------------------ In sklearn/preprocessing/target.py <#9041 (comment)> : > + ---------- + estimator : object, (default=LinearRegression()) + Estimator object derived from ``RegressorMixin``. + + transformer : object, (default=None) + Estimator object derived from ``TransformerMixin``. Cannot be set at + the same time as ``func`` and ``inverse_func``. If ``None`` and + ``func`` and ``inverse_func`` are ``None`` as well, the transformer + will be an identity transformer. + + func : function, (default=None) + Function to apply to ``y`` before passing to ``fit``. Cannot be set at + the same time than ``transformer``. If ``None`` and ``transformer`` is + ``None`` as well, the function used will be the identity function. + + inverse_func : function, (default=None) Since None will lead to the identity function and that we don't enforce func and inverse_func to be be actually the inverse of each other, I am not sure that inverse_func should be required. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#9041 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAEz66KUL8Dsk0gJ8GD5sNskIppWzVzvks5sB9dggaJpZM4NywA7> .
changed the title from
Jun 8, 2017
fit_transform is mostly used for efficiency…
On 9 Jun 2017 10:14 am, "Guillaume Lemaitre" ***@***.***> wrote: @jnothman <https://github.com/jnothman> I miss the what's new but I added some doc and address almost all comments. I am just unsure about fit + transform vs fit_transform and there implications. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#9041 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAEz6_rPW2YLTp-Io7H1I2ev5xFXULsmks5sCI5kgaJpZM4NywA7> .
referenced this pull request
Nov 28, 2017
A couple of small things to test. And the naming/placement questions stand. Apart from which LGTM.
referenced this pull request
Nov 29, 2017
Dec 13, 2017
6 checks passed
One less hack for my class! This is moving forward quite nicely lol. (PowerTransformer was another). Can we do KNN imputation, missing value features and ColumnTransformer next? Oh and blanced random forests (though actually imblearn has it now :)? I think then I'm good... just need to implement a decent time series library in python, or something...