Also Sebastian, I have binary classes but they are strings: clf.classes_:
array(['American', 'Southwest'], dtype=object) On Tue, Jan 8, 2019 at 9:51 AM pisymbol <pisym...@gmail.com> wrote: > If that is the case, what order are the coefficients in then? > > -aps > > On Tue, Jan 8, 2019 at 12:48 AM Sebastian Raschka < > m...@sebastianraschka.com> wrote: > >> E.g, if you have a feature with values 'a' , 'b', 'c', then applying the >> one hot encoder will transform this into 3 features. >> >> Best, >> Sebastian >> >> > On Jan 7, 2019, at 11:02 PM, pisymbol <pisym...@gmail.com> wrote: >> > >> > >> > >> > On Mon, Jan 7, 2019 at 11:50 PM pisymbol <pisym...@gmail.com> wrote: >> > According to the doc (0.20.2) the coef_ variables are suppose to be >> shape (1, n_features) for binary classification. Well I created a Pipeline >> and performed a GridSearchCV to create a LogisticRegresion model that does >> fairly well. However, when I want to rank feature importance I noticed that >> my coefs_ for my best_estimator_ has 24 entries while my training data has >> 22. >> > >> > What am I missing? How could coef_ > n_features? >> > >> > >> > Just a follow-up, I am using a OneHotEncoder to encode two categoricals >> as part of my pipeline (I am also using an imputer/standard scaler too but >> I don't see how that could add features). >> > >> > Could my pipeline actually add two more features during fitting? >> > >> > -aps >> > _______________________________________________ >> > scikit-learn mailing list >> > scikit-learn@python.org >> > https://mail.python.org/mailman/listinfo/scikit-learn >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >
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