Thanks for your reply. How I can set the name of the positive class in LinearSVC() for a two-class problem that when using the prediction then I will get positive scores fro that positive class?
Malik --------------------------------------------------------------------------------------- *Prof. Malik Yousef , Associate Professor * *Zefat Academic College , Department of Information System * Home Page: https://malikyousef.com/ Google Scholar Profile : https://scholar.google.com/citations?user=9UCZ_q4AAAAJ&hl=en&oi=ao ---------------------------------------------------------------------------------------------------- On Thu, Aug 1, 2019 at 8:43 PM Joel Nothman <joel.noth...@gmail.com> wrote: > Or use scoring=make_scorer(f1_score, pos_label='n.pre') > > On Fri, 2 Aug 2019 at 06:15, Malik Yousef <malik.you...@gmail.com> wrote: > >> Hello >> When in using the scoring to be 'f1' then i get an error. >> Here is the code and the error >> >> X=data >> y=target_column >> classifier = LinearSVC() >> rfecv = RFECV(estimator=classifier, step=0.1, >> cv=StratifiedKFold(5),scoring='f1') >> rfecv.fit(X, y) >> >> The error is : >> ValueError: pos_label=1 is not a valid label: array([u'c.pre', u'n.pre'], >> dtype='<U5') >> >> Please your help >> (I'm new to using scikit-leanr) >> >> --------------------------------------------------------------------------------------- >> *Prof. Malik Yousef , Associate Professor * >> *Zefat Academic College , Department of Information System * >> Home Page: >> https://malikyousef.com/ >> Google Scholar Profile : >> https://scholar.google.com/citations?user=9UCZ_q4AAAAJ&hl=en&oi=ao >> >> ---------------------------------------------------------------------------------------------------- >> _______________________________________________ >> 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 >
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