[scikit-learn] LogisticRegression coef_ greater than n_features?

[pisymbol]

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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