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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