[scikit-learn] LogisticRegression coef_ greater than n_features?

[pisymbol]

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 <pisymbol at 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 <
> mail at 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 <pisymbol at gmail.com> wrote:
>> >
>> >
>> >
>> > On Mon, Jan 7, 2019 at 11:50 PM pisymbol <pisymbol at 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
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