Maybe check a) if the actual labels of the training examples don't start at 0 b) if you have gaps, e.g,. if your unique training labels are 0, 1, 4, ..., 23
Best, Sebastian > On Jan 7, 2019, at 10:50 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? > > -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
