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
