  We present an updated, optimized version of GAME (GAlaxy Machine learning for
Emission lines), a code designed to infer key interstellar medium physical
properties from emission line intensities of UV/optical/far infrared galaxy
spectra. The improvements concern: (a) an enlarged spectral library including
Pop III stars; (b) the inclusion of spectral noise in the training procedure,
and (c) an accurate evaluation of uncertainties. We extensively validate the
optimized code and compare its performance against empirical methods and other
available emission line codes (PYQZ and HII-CHI-MISTRY) on a sample of 62 SDSS
stacked galaxy spectra and 75 observed HII regions. Very good agreement is
found for metallicity. However, ionization parameters derived by GAME tend to
be higher. We show that this is due to the use of too limited libraries in the
other codes. The main advantages of GAME are the simultaneous use of all the
measured spectral lines, and the extremely short computational times. We
finally discuss the code potential and limitations.
