we have to be patient when executing the following codeclf grid_search_model(create_ngram_modelxyprint clf this is because we have just requested parameter sweep over the parameter combinations--each being trained on foldswaiting some hours pipeline(clf=multinomialnbalpha= class_weight=nonefit_prior=true)clf__alpha= clf__class_weight=noneclf__fit_prior=truevect=tfidfvectorizeranalyzer=wordbinary=falsecharset=utf- charset_error=strictdtype=input=contentlowercase=truemax_df= max_features=nonemax_n=nonemin_df= min_n=nonengram_range=( )norm= preprocessor=nonesmooth_idf=falsestop_words=none,strip_accents=nonesublinear_tf=truetoken_pattern=(? )\ \ \ +\btoken_processor=nonetokenizer=noneuse_idf=falsevocabulary=none)vect__analyzer=wordvect__binary=falsevect__charset=utf- vect__charset_error=strictvect__dtype=vect__input=contentvect__lowercase=truevect__max_df= vect__max_features=nonevect__max_n=nonevect__min_df= vect__min_n=nonevect__ngram_range=( )vect__norm= vect__preprocessor=nonevect__smooth_idf=falsevect__stop_words=nonevect__strip_accents=nonevect__sublinear_tf=truevect__token_pattern=(? )\ \ \ +\bvect__token_processor=nonevect__tokenizer=nonevect__use_idf=falsevect__vocabulary=none the best estimator indeed improves the / auc by nearly percent to with the setting that was printed earlier