Yes, it's on a hold out segment from the data set being fitted. On Wed, Sep 7, 2016 at 1:02 AM Sean Owen <so...@cloudera.com> wrote:
> Yes, should be. > It's also not necessarily nonnegative if you evaluate R^2 on a > different data set than you fit it to. Is that the case? > > On Tue, Sep 6, 2016 at 11:15 PM, Evan Zamir <zamir.e...@gmail.com> wrote: > > I am using the default setting for setting fitIntercept, which *should* > be > > TRUE right? > > > > On Tue, Sep 6, 2016 at 1:38 PM Sean Owen <so...@cloudera.com> wrote: > >> > >> Are you not fitting an intercept / regressing through the origin? with > >> that constraint it's no longer true that R^2 is necessarily > >> nonnegative. It basically means that the errors are even bigger than > >> what you'd get by predicting the data's mean value as a constant > >> model. > >> > >> On Tue, Sep 6, 2016 at 8:49 PM, evanzamir <zamir.e...@gmail.com> wrote: > >> > Am I misinterpreting what r2() in the LinearRegression Model summary > >> > means? > >> > By definition, R^2 should never be a negative number! > >> > > >> > > >> > > >> > -- > >> > View this message in context: > >> > > http://apache-spark-user-list.1001560.n3.nabble.com/I-noticed-LinearRegression-sometimes-produces-negative-R-2-values-tp27667.html > >> > Sent from the Apache Spark User List mailing list archive at > Nabble.com. > >> > > >> > --------------------------------------------------------------------- > >> > To unsubscribe e-mail: user-unsubscr...@spark.apache.org > >> > >
