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 > --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
